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  • 1.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. RISE SICS, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks2018Conference paper (Refereed)
    Abstract [en]

    Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing amounts of data and growing cities. We propose and compare three models for short-term road traffic density prediction based on Long Short-Term Memory (LSTM) neural networks. We have trained the models using real traffic data collected by Motorway Control System in Stockholm that monitors highways and collects flow and speed data per lane every minute from radar sensors. In order to deal with the challenge of scale and to improve prediction accuracy, we propose to partition the road network into road stretches and junctions, and to model each of the partitions with one or more LSTM neural networks. Our evaluation results show that partitioning of roads improves the prediction accuracy by reducing the root mean square error by the factor of 5. We show that we can reduce the complexity of LSTM network by limiting the number of input sensors, on average to 35% of the original number, without compromising the prediction accuracy.

  • 2.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Ivarsson, Jón Reginbald
    KTH.
    Al-Shishtawy, A.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Scaling Deep Learning Models for Large Spatial Time-Series Forecasting:
    2019In: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019:
    , Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1587-1594
    Conference paper (Refereed)
    Abstract [en]

    Neural networks are used for different machine learning tasks, such as spatial time-series forecasting. Accurate modelling of a large and complex system requires large datasets to train a deep neural network that causes a challenge of scale as training the network and serving the model are computationally and memory intensive. One example of a complex system that produces a large number of spatial time-series is a large road sensor infrastructure deployed for traffic monitoring. The goal of this work is twofold: 1) To model large amount of spatial time-series from road sensors; 2) To address the scalability problem in a real-life task of large-scale road traffic prediction which is an important part of an Intelligent Transportation System.We propose a partitioning technique to tackle the scalability problem that enables parallelism in both training and prediction: 1) We represent the sensor system as a directed weighted graph based on the road structure, which reflects dependencies between sensor readings, and weighted by sensor readings and inter-sensor distances; 2) We propose an algorithm to automatically partition the graph taking into account dependencies between spatial time-series from sensors; 3) We use the generated sensor graph partitions to train a prediction model per partition. Our experimental results on traffic density prediction using Long Short-Term Memory (LSTM) Neural Networks show that the partitioning-based models take 2x, if run sequentially, and 12x, if run in parallel, less training time, and 20x less prediction time compared to the unpartitioned model of the entire road infrastructure. The partitioning-based models take 100x less total sequential training time compared to single sensor models, i.e., one model per sensor. Furthermore, the partitioning-based models have 2x less prediction error (RMSE) compared to both the single sensor models and the entire road model. 

  • 3.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Kalavri, Vasiliki
    Systems Group, ETH, Zurich, Switzerland.
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Streaming Graph Partitioning: An Experimental Study2018In: Proceedings of the VLDB Endowment, E-ISSN 2150-8097, Vol. 11, no 11, p. 1590-1603Article in journal (Refereed)
    Abstract [en]

    Graph partitioning is an essential yet challenging task for massive graph analysis in distributed computing. Common graph partitioning methods scan the complete graph to obtain structural characteristics offline, before partitioning. However, the emerging need for low-latency, continuous graph analysis led to the development of online partitioning methods. Online methods ingest edges or vertices as a stream, making partitioning decisions on the fly based on partial knowledge of the graph. Prior studies have compared offline graph partitioning techniques across different systems. Yet, little effort has been put into investigating the characteristics of online graph partitioning strategies.

    In this work, we describe and categorize online graph partitioning techniques based on their assumptions, objectives and costs. Furthermore, we employ an experimental comparison across different applications and datasets, using a unified distributed runtime based on Apache Flink. Our experimental results showcase that model-dependent online partitioning techniques such as low-cut algorithms offer better performance for communication-intensive applications such as bulk synchronous iterative algorithms, albeit higher partitioning costs. Otherwise, model-agnostic techniques trade off data locality for lower partitioning costs and balanced workloads which is beneficial when executing data-parallel single-pass graph algorithms.

  • 4.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Sigurdsson, Thorsteinn Thorri
    KTH.
    Al-Shishtawy, Ahmad
    RISE Res Inst Sweden, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection2018In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1017-1025Conference paper (Refereed)
    Abstract [en]

    Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to approximate to 94% for Connected Components and up to approximate to 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.

  • 5.
    Ahlberg, Michael
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Yasui, Terumasa
    Advanced Technology Research and Development Center, Mitsubishi Electric Corporation, Amagasaki, Japan.
    Router placement in wireless sensor networks2006In: 2006 IEEE International Conference on Mobile Adhoc and Sensor Systems, Vols 1 and 2, IEEE , 2006, p. 498-501Conference paper (Refereed)
    Abstract [en]

    In this paper we propose and evaluate algorithms for placement of routers in a wireless sensor network. There are two major requirements on router placement First, a placement must guarantee connectivity, i.e. every sensor must be able to communicate through routers with a predefined computer-connected gateway node. Second, a placement must provide robust communication in the case of router failures. This is achieved by placing redundant routers that increase the number of possible routes. Both requirements should be met by placing as few routers as possible. The proposed algorithms compute placement in an efficient and reasonably fast way.

  • 6.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Asif Fayyaz, Muhammad
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Popov, Konstantin
    Swedish Institute of Computer Science.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Achieving robust self-management for large-scale distributed applications2010Report (Other (popular science, discussion, etc.))
    Abstract [en]

    Autonomic managers are the main architectural building blocks for constructing self-management capabilities of computing systems and applications. One of the major challenges in developing self-managing applications is robustness of management elements which form autonomic managers. We believe that transparent handling of the effects of resource churn (joins/leaves/failures) on management should be an essential feature of a platform for selfmanaging large-scale dynamic distributed applications, because it facilitates the development of robust autonomic managers and hence improves robustness of self-managing applications. This feature can be achieved by providing a robust management element abstraction that hides churn from the programmer. In this paper, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of nodes hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn. Our proposed decentralized algorithm uses peer-to-peer replica placement schemes to automate replicated state machine migration in order to tolerate churn. Our algorithm exploits lookup and failure detection facilities of a structured overlay network for managing the set of active replicas. Using the proposed approach, we can achieve a long running and highly available service, without human intervention, in the presence of resource churn. In order to validate and evaluate our approach, we have implemented a prototype that includes the proposed algorithm.

     

  • 7.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Bao, Lin
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Policy based self-management in distributed environments2010In: 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop (SASOW), IEEE Computer Society Digital Library, 2010, p. 256-260Conference paper (Refereed)
    Abstract [en]

      Currently, increasing costs and escalating complexities are primary issues in the distributed system management. The policy based management is introduced to simplify the management and reduce the overhead, by setting up policies to govern system behaviors. Policies are sets of rules that govern the system behaviors and reflect the business goals or system management objectives. This paper presents a generic policy-based management framework which has been integrated into an existing distributed component management system, called Niche, that enables and supports self-management. In this framework, programmers can set up more than one Policy-Manager-Group to avoid centralized policy decision making which could become a performance bottleneck. Furthermore, the size of a Policy-Manager-Group, i.e. the number of Policy-Managers in the group, depends on their load, i.e. the number of requests per time unit. In order to achieve good load balancing, a policy request is delivered to one of the policy managers in the group randomly chosen on the fly. A prototype of the framework is presented and two generic policy languages (policy engines and corresponding APIs), namely SPL and XACML, are evaluated using a self-managing file storage application as a case study.

  • 8.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Fayyaz, Muhammad Asif
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Achieving Robust Self-Management for Large-Scale Distributed Applications2010In: Self-Adaptive and Self-Organizing Systems (SASO), 2010 4th IEEE International Conference on: SASO 2010, IEEE Computer Society, 2010, p. 31-40Conference paper (Refereed)
    Abstract [sv]

    Achieving self-management can be challenging, particularly in dynamic environments with resource churn (joins/leaves/failures). Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of robust management elements (RMEs), which are able to heal themselves under continuous churn. Using RMEs allows the developer to separate the issue of dealing with the effect of churn on management from the management logic. This facilitates the development of robust management by making the developer focus on managing the application while relying on the platform to provide the robustness of management. RMEs can be implemented as fault-tolerant long-living services. We present a generic approach and an associated algorithm to achieve fault-tolerant long-living services. Our approach is based on replicating a service using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. The algorithm uses P2P replica placement schemes to place replicas and uses the P2P overlay to monitor them. The replicated state machine is extended to analyze monitoring data in order to decide on when and where to migrate. We describe how to use our approach to achieve robust management elements. We present a simulation-based evaluation of our approach which shows its feasibility.

  • 9.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Höglund, Joel
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Parlavantzas, Nikos
    INRIA, Grenoble, France.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Distributed Control Loop Patterns for Managing Distributed Applications2008In: SASOW 2008: SECOND IEEE INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS, PROCEEDINGS / [ed] Serugendo GD, LOS ALAMITOS: IEEE Computer Society, 2008, p. 260-265Conference paper (Refereed)
    Abstract [en]

    In this paper we discuss various control loop patterns for managing distributed applications with multiple control loops. We introduce a high-level framework, called DCMS, for developing, deploying and managing component-based distributed applications in dynamic environments. The control loops, and interactions among them, are illustrated in the context of a distributed self-managing storage service implemented using DCMS to achieve various self-* properties. Different control loops are used for different self-* behaviours, which illustrates one way to divide application management, which makes for both ease of development and for better scalability and robustness when managers are distributed. As the multiple control loops are not completely independent, we demonstrate different patterns to deal with the interaction and potential conflict between multiple managers.

  • 10.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Höglund, Joel
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Parlavantzas, Nikos
    INRIA, Grenoble, France.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Enabling Self-Management Of Component Based Distributed Applications2008In: FROM GRIDS TO SERVICE AND PERVASIVE COMPUTING, Springer-Verlag New York, 2008, p. 163-174Conference paper (Refereed)
    Abstract [en]

    Deploying and managing distributed applications in dynamic Grid environments requires a high degree of autonomous management. Programming autonomous management in turn requires programming environment support and higher level abstractions to become feasible. We present a framework for programming self-managing component-based distributed applications. The framework enables the separation of application’s functional and non-functional (self-*) parts. The framework extends the Fractal component model by the component group abstraction and one-to-any and one-to-all bindings between components and groups. The framework supports a network-transparent view of system architecture simplifying designing application self-* code. The framework provides a concise and expressive API for self-* code. The implementation of the framework relies on scalability and robustness of the Niche structured p2p overlay network. We have also developed a distributed file storage service to illustrate and evaluate our framework.

  • 11.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Khan, Tareq Jamal
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Robust Fault-Tolerant Majority-Based Key-Value Store Supporting Multiple Consistency Levels2011In: 2011 IEEE 17TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2011, p. 589-596Conference paper (Refereed)
    Abstract [en]

    The wide spread of Web 2.0 applications with rapidly growing amounts of user generated data, such as, wikis, social networks, and media sharing, have posed new challenges on the supporting infrastructure, in particular, on storage systems. In order to meet these challenges, Web 2.0 applications have to tradeoff between the high availability and the consistency of their data. Another important issue is the privacy of user generated data that might be caused by organizations that own and control datacenters where user data are stored. We propose a large-scale, robust and fault-tolerant key-value object store that is based on a peer-to-peer network owned and controlled by a community of users. To meet the demands of Web 2.0 applications, the store supports an API consisting of different read and write operations with various data consistency guarantees from which a wide range of web applications would be able to choose the operations according to their data consistency, performance and availability requirements. For evaluation, simulation has been carried out to test the system availability, scalability and fault-tolerance in a dynamic, Internet wide environment.

  • 12.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    ElastMan: Autonomic elasticity manager for cloud-based key-value stores2013In: HPDC 2013 - Proceedings of the 22nd ACM International Symposium on High-Performance Parallel and Distributed Computing, 2013, p. 115-116Conference paper (Refereed)
    Abstract [en]

    The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud virtual machines and nonlinearities in Cloud services complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. We have implemented and evaluated ElastMan using the Voldemort key-value store running in a Cloud environment based on OpenStack. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.

  • 13.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    ElastMan: Autonomic Elasticity Manager for Cloud-Based Key-Value Stores2012Report (Other academic)
    Abstract [en]

    The increasing spread of elastic Cloud services, together with the pay-asyou-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service, in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud virtual machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. Our design based on combining feedforward and feedback control allows to efficiently handle both diurnal and rapid changes in workload in order to meet SLOs at a minimal cost. Our evaluation shows the feasibility of our approach to automation of Cloud service elasticity.

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  • 14.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    ElastMan: Elasticity manager for elastic key-value stores in the cloud2013In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, New York, NY, USA: Association for Computing Machinery (ACM), 2013, p. 7:1-7:10Conference paper (Refereed)
    Abstract [en]

    The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud Virtual Machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. We have implemented and evaluated ElastMan using the Voldemort key-value store running in an OpenStack Cloud environment. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.

  • 15.
    Al-Shishtawy, Ahmad
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Brand, Per
    Swedish Institute of Computer Science.
    Haridi, Seif
    Swedish Institute of Computer Science.
    A design methodology for self-management in distributed environments2009In: IEEE International conference on Computational Science and Engineering, 2009, p. 430-436Conference paper (Refereed)
    Abstract [en]

      Autonomic computing is a paradigm that aims at reducing administrative overhead by providing autonomic managers to make applications selfmanaging. In order to better deal with dynamic environments, for improved performance and scalability, we advocate for distribution of management functions among several cooperative managers that coordinate their activities in order to achieve management objectives. We present a methodology for designing the management part of a distributed self-managing application in a distributed manner. We define design steps, that includes partitioning of management functions and orchestration of multiple autonomic managers. We illustrate the proposed design methodology by applying it to design and development of a distributed storage service as a case study. The storage service prototype has been developed using the distributing component management system Niche. Distribution of autonomic managers allows distributing the management overhead and increased management performance due to concurrency and better locality.

  • 16.
    Apolonia, Nuno
    et al.
    Universitat Politecnica de Catalunya (UPC) Barcelona, Spain.
    Freitag, Felix
    Universitat Politècnica de Catalunya. Barcelona, Spain.
    Navarro, Leandro
    Universitat Politècnica de Catalunya, BarcelonaTECH, Barcelona, Spain.
    Girdzijauskas, Sarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Gossip-based service monitoring platform for wireless edge cloud computing2017In: Proceedings IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Institute of Electrical and Electronics Engineers (IEEE) , 2017Conference paper (Refereed)
    Abstract [en]

    Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.

  • 17.
    Arman, Ala
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Al-Shishtawy, Ahmad
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Elasticity controller for Cloud-based key-value stores2012In: Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, IEEE , 2012, p. 268-275Conference paper (Refereed)
    Abstract [en]

    Clouds provide an illusion of an infinite amount of resources and enable elastic services and applications that are capable to scale up and down (grow and shrink by requesting and releasing resources) in response to changes in its environment, workload, and Quality of Service (QoS) requirements. Elasticity allows to achieve required QoS at a minimal cost in a Cloud environment with its pay-as-you-go pricing model. In this paper, we present our experience in designing a feedback elastically controller for a key-value store. The goal of our research is to investigate the feasibility of the control theoretic approach to the automation of elasticity of Cloud-based key-value stores. We describe design steps necessary to build a feedback controller for a real system, namely Voldemort, which we use as a case study in this work. The design steps include defining touchpoints (sensors and actuators), system identification, and controller design. We have designed, developed, and implemented a prototype of the feedback elasticity controller for Voldemort. Our initial evaluation results show the feasibility of using feedback control to automate elasticity of distributed keyvalue stores.

  • 18.
    Arsalan, Muhammad
    et al.
    Tech Univ Carolo Wilhelmina Braunschweig, Braunschweig, Germany..
    Di Matteo, Davide
    KTH.
    Imtiaz, Sana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. KRY Int AB, Stockholm, Sweden..
    Abbas, Zainab
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. KRY Int AB, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Issakov, Vadim
    Tech Univ Carolo Wilhelmina Braunschweig, Braunschweig, Germany..
    Energy-Efficient Privacy-Preserving Time-Series Forecasting on User Health Data Streams2022In: Proceedings - 2022 IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 541-546Conference paper (Refereed)
    Abstract [en]

    Health monitoring devices are gaining popularity both as wellness tools and as a source of information for healthcare decisions. In this work, we use Spiking Neural Networks (SNNs) for time-series forecasting due to their proven energy-saving capabilities. Thanks to their design that closely mimics the natural nervous system, SNNs are energy-efficient in contrast to classic Artificial Neural Networks (ANNs). We design and implement an energy-efficient privacy-preserving forecasting system on real-world health data streams using SNNs and compare it to a state-of-the-art system with Long short-term memory (LSTM) based prediction model. Our evaluation shows that SNNs tradeoff accuracy (2.2x greater error), to grant a smaller model (19% fewer parameters and 77% less memory consumption) and a 43% less training time. Our model is estimated to consume 3.36 mu J energy, which is significantly less than the traditional ANNs. Finally, we apply epsilon-differential privacy for enhanced privacy guarantees on our federated learning-based models. With differential privacy of epsilon = 0.1, our experiments report an increase in the measured average error (RMSE) of only 25%.

  • 19.
    Asratyan, Albert
    et al.
    KTH.
    Sheikholeslami, Sina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    A Parallel Chain Mail Approach for Scalable Spatial Data Interpolation2021In: 2021 IEEE International Conference on Big Data (Big Data), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 306-314Conference paper (Refereed)
    Abstract [en]

    Deteriorating air quality is a growing concern that has been linked to many health-related issues. Its monitoring is a good first step to understanding the problem. However, it is not always possible to collect air quality data from every location. Various data interpolation techniques are used to assist with populating sparse maps with more context, but many of these algorithms are computationally expensive. This work introduces a three-step Chain Mail algorithm that uses kriging (without any modifications to the base algorithm) and achieves up to ×100 execution time improvement with minimal accuracy loss (relative RMSE of 3%) by running concurrent interpolation executions. This approach can be described as a multiple-step parallel interpolation algorithm that includes specific regional border data manipulation for achieving greater accuracy. It does so by interpolating geographically defined data chunks in parallel and sharing the results with their neighboring nodes to provide context and compensate for lack of knowledge of the surrounding areas. Combined with a serverless cloud architecture, this approach opens doors to interpolating large data sets in a matter of minutes while remaining cost-efficient. The effectiveness of the three-step Chain Mail approach depends on the equal point distribution among all nodes and the resolution of the parallel configuration. In general, it offers a good balance between execution speed and accuracy.

  • 20.
    Attieh, Joseph
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. University of Helsinki, Helsinki, Finland;Huawei Technologies Oy., Helsinki, Finland.
    Woubie Zewoudie, Abraham
    Silo AI, Helsinki, Finland.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Flanagan, Adrian
    Huawei Technologies Oy., Helsinki, Finland.
    Bäckström, Tom
    Aalto University, Espoo, Finland.
    Optimizing the Performance of Text Classification Models by Improving the Isotropy of the Embeddings Using a Joint Loss Function2023In: Document Analysis and Recognition: ICDAR 2023 / [ed] Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, Cham: Springer Nature , 2023, p. 121-136Conference paper (Refereed)
    Abstract [en]

    Recent studies show that the spatial distribution of the sentence representations generated from pre-trained language models is highly anisotropic. This results in a degradation in the performance of the models on the downstream task. Most methods improve the isotropy of the sentence embeddings by refining the corresponding contextual word representations, then deriving the sentence embeddings from these refined representations. In this study, we propose to improve the quality of the sentence embeddings extracted from the [CLS] token of the pre-trained language models by improving the isotropy of the embeddings. We add one feed-forward layer between the model and the downstream task layers, and we train it using a novel joint loss function. The proposed approach results in embeddings with better isotropy, that generalize better on the downstream task. Experimental results on 3 GLUE datasets with classification as the downstream task show that our proposed method is on par with the state-of-the-art, as it achieves performance gains of around 2–3% on the downstream tasks compared to the baseline.

  • 21.
    Awan, Ahsan Javed
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Brorsson, Mats
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Architectural Impact on Performance of In-memoryData Analytics: Apache Spark Case StudyManuscript (preprint) (Other academic)
    Abstract [en]

    While cluster computing frameworks are contin-uously evolving to provide real-time data analysis capabilities,Apache Spark has managed to be at the forefront of big data an-alytics for being a unified framework for both, batch and streamdata processing. However, recent studies on micro-architecturalcharacterization of in-memory data analytics are limited to onlybatch processing workloads. We compare micro-architectural per-formance of batch processing and stream processing workloadsin Apache Spark using hardware performance counters on a dualsocket server. In our evaluation experiments, we have found thatbatch processing are stream processing workloads have similarmicro-architectural characteristics are bounded by the latency offrequent data access to DRAM. For data accesses we have foundthat simultaneous multi-threading is effective in hiding the datalatencies. We have also observed that (i) data locality on NUMAnodes can improve the performance by 10% on average and(ii)disabling next-line L1-D prefetchers can reduce the executiontime by up-to 14% and (iii) multiple small executors can provideup-to 36% speedup over single large executor

    Download full text (pdf)
    paper
  • 22.
    Awan, Ahsan Javed
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Brorsson, Mats
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ayguade, Eduard
    Technical University of Catalunya, Barcelona Super Computing Center.
    How Data Volume Affects Spark Based Data Analytics on a Scale-up Server2015In: Big Data Benchmarks, Performance Optimization, and Emerging Hardware: 6th Workshop, BPOE 2015, Kohala, HI, USA, August 31 - September 4, 2015. Revised Selected Papers, Springer, 2015, Vol. 9495, p. 81-92Conference paper (Refereed)
    Abstract [en]

    Sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well understood. We present a deep-dive analysis of Spark based applications on a large scale-up server machine. Our analysis reveals that Spark based data analytics are DRAM bound and do not benefit by using more than 12 cores for an executor. By enlarging input data size, application performance degrades significantly due to substantial increase in wait time during I/O operations and garbage collection, despite 10 % better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behaviour with the garbage collector to improve performance of applications between 1.6x to 3x.

    Download full text (pdf)
    fulltext
  • 23.
    Awan, Ahsan Javed
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Brorsson, Mats
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Micro-architectural Characterization of Apache Spark on Batch and Stream Processing Workloads2016Conference paper (Refereed)
    Abstract [en]

    While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. However, recent studies on micro-architectural characterization of in-memory data analytics are limited to only batch processing workloads. We compare the micro-architectural performance of batch processing and stream processing workloads in Apache Spark using hardware performance counters on a dual socket server. In our evaluation experiments, we have found that batch processing and stream processing has same micro-architectural behavior in Spark if the difference between two implementations is of micro-batching only. If the input data rates are small, stream processing workloads are front-end bound. However, the front end bound stalls are reduced at larger input data rates and instruction retirement is improved. Moreover, Spark workloads using DataFrames have improved instruction retirement over workloads using RDDs.

  • 24.
    Awan, Ahsan Javed
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Brorsson, Mats
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Node architecture implications for in-memory data analytics on scale-in clusters2016Conference paper (Refereed)
    Abstract [en]

    While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics. Recent studies propose scale-in clusters with in-storage processing devices to process big data analytics with Spark However the proposal is based solely on the memory bandwidth characterization of in-memory data analytics and also does not shed light on the specification of host CPU and memory. Through empirical evaluation of in-memory data analytics with Apache Spark on an Ivy Bridge dual socket server, we have found that (i) simultaneous multi-threading is effective up to 6 cores (ii) data locality on NUMA nodes can improve the performance by 10% on average, (iii) disabling next-line L1-D prefetchers can reduce the execution time by up to 14%, (iv) DDR3 operating at 1333 MT/s is sufficient and (v) multiple small executors can provide up to 36% speedup over single large executor.

  • 25.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Carbajales, Rodrigo
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    Escrich, Pau
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Florit, Jorge
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Moll, Agusti
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Pietrosemoli, Ermanno
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    Pueyo, Roger
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Selimi, Mennan
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Zennaro, Marco
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    The Cloudy Distribution in Community Network Clouds in Guifi.net2015Conference paper (Refereed)
    Abstract [en]

    This demo paper presents Cloudy, a Debian-based distribution to build and deploy clouds incommunity networks. The demonstration covers the following aspects: Installation of Cloudy, theCloudy GUI for usage and administration by end users, demonstration of Cloudy nodes and services deployed in the Guifi community network.

  • 26.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Dowling, Jim
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Escrich, Pau
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Meseguer, Roc
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Moll, Agusti
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Pietrosemoli, Ermanno
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    Pueyo, Roger
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Zennaro, Marco
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    Deploying Clouds in the Guifi Community Network2015In: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, IEEE , 2015, p. 1020-1025Conference paper (Refereed)
    Abstract [en]

    This paper describes an operational geographically distributed and heterogeneous cloudinfrastructure with services and applications deployed in the Guifi community network. The presentedcloud is a particular case of a community cloud, developed according to the specific needs and conditions of community networks. We describe the concept of this community cloud, explain our technical choices for building it, and our experience with the deployment of this cloud. We review our solutions and experience on offering the different service models of cloud computing (IaaS, PaaS and SaaS) in community networks. The deployed cloud infrastructure aims to provide stable and attractive cloud services in order to encourage community network user to use, keep and extend it with new services and applications.

  • 27.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Khan, Amin M.
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Moll, Agusti
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Pueyo, Roger
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Community clouds at the edge deployed in Guifi.net2015Conference paper (Refereed)
    Abstract [en]

    Community clouds are a cloud deployment model in which the cloud infrastructure is built with specific features for a community of users with shared concerns, goals, and interests. Commercialcommunity clouds already operate in several application areas such as in the finance, government and health, fulfilling community-specific requirements. In this demo, a community cloud for citizens is presented. It is formed by devices at the edge of the network, contributed by the members of acommunity network and brought together into a distributed community cloud system through the Cloudy distribution. The demonstration shows to the audience in a live access the deployedcommunity cloud from the perspective of the user, by accessing a Cloudy node, inspecting the services available in the community cloud, and showing the usage of some of its services.

  • 28.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Moll, Agusti
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Pueyo, Roger
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Cloud-based community services in community networks2016In: 2016 International Conference on Computing, Networking and Communications, ICNC 2016, IEEE conference proceedings, 2016, p. 1-5, article id 7440621Conference paper (Refereed)
    Abstract [en]

    Wireless networks have shown to be a cost effective solution for an IP-based communication infrastructure in under-served areas. Services and application, if deployed within these wireless networks, add value for the users. This paper shows how cloud infrastructures have been made operational in a community wireless network, as a particular case of a community cloud, developed according to the specific requirements and conditions of the community. We describe the conditions and requirements of such a community cloud and explain our technical choices and experience in its deployment in the community network. The user take-up has started, and our case supports the tendency of cloud computing moving towards the network edge.

  • 29.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Moll, Agusti
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Pueyo, Roger
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Community network clouds as a case for the IEEE Intercloud standardization2015In: 2015 IEEE Conference on Standards for Communications and Networking, CSCN 2015, 2015, p. 269-274, article id 7390456Conference paper (Refereed)
    Abstract [en]

    The IEEE P2302 Intercloud WG conducts work since 2011 on the project Standard for Intercloud Interoperability and Federation with the goal to define a standard architecture and building components for large-scale interoperability of independent cloud providers. While the standardization process has achieved fine-grained definitions of several Intercloud components, a deployment of the Intercloud to demonstrate the architectural feasibility is not yet operational. In this paper, we describe a deployed community network cloud and we show how it matches in several aspects the vision of the Intercloud. Similar to the Intercloud, the community network cloud consists of many small cloud providers, which for interoperability use a set of common services. In this sense, the community network cloud is a real use case for elements that the Intercloud standardization WG envisions, and can feed back to and even become part of the Intercloud. In fact, a study on Small or Medium Enterprise (SME) provided commercial services in the community network cloud indicates the importance of the success of the Intercloud standardization initiative for SMEs.

  • 30. Baig, Roger
    et al.
    Freitag, Felix
    Moll, Agusti
    Navarro, Leandro
    Pueyo, Roger
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Experiences in Building Micro-Cloud Provider Federation in the Guifi Community Network2015In: 2015 IEEE/ACM 8TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2015, p. 516-521Conference paper (Refereed)
    Abstract [en]

    Cloud federation is foreseen to happen among large cloud providers. The resulting interoperability of cloud services among these providers will then increase even more the elasticity of cloud services. The cloud provisioned that is targeted by this scenario is mainly one which combines the cloud services offered by large enterprises. Cloud computing, however, has started moving to the edge. We now increasingly see the tendency to fullfil cloud computing requirements by multiple levels and different kind of infrastructures, where the Fog Computing paradigm has started playing its role. For this scenario of edge computing, we show in this paper the case of the federation of multiple independent micro-cloud providers within a community network, where providers pool their resources and services into a community cloud. Federation happens here primarily at the service level and the domain of trust is the community of practice. While we can today already report this case in the context of community networks, IPv6 deployment in the Internet will principally allow micro-cloud providers to appear everywhere, needing cloud federation mechanisms. We describe for a real case how this micro-cloud provider federation has been built and argue why micro-cloud provider should be considered for the integration in cloud federations.

  • 31.
    Brand, Per
    et al.
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Höglund, Joel
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    de Palma, Noel
    INRIA, France.
    Boyer, Fabienne
    INRIA, France.
    Parlavantzas, Nikos
    INRIA, France.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Al-Shishtawy, Ahmad
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    The Role of Overlay Services In a Self-Managing Framework for Dynamic Virtual Organizations2008In: Making Grids Work: Proceedings of the CoreGRID Workshop on Programming Models Grid and P2P System Architecture Grid Systems, Tools and Environments / [ed] Marco Danelutto, Paraskevi Fragopoulou and Vladimir Getov, Springer-Verlag New York, 2008, p. 153-164Conference paper (Refereed)
    Abstract [en]

    We combine and extend recent results in autonomic computing and structuredpeer-to-peer to build an infrastructure for constructing and managing dynamic vir-tual organizations. The paper focuses on the middle layer of the proposed infras-tructure, in-between the Niche overlay system on the bottom, and an architecture-based management system based on Jade on the top.  The middle layer, theoverlay services, are responsible for all sensing and actuation carried out by theVO management. We describe in detail the API of the resource and componentoverlay services both on the management node and the nodes hosting resources.We present a simple use case demonstrating resource discovery, initial deploy-ment, self-configuration as a result of resource availability change, self-healing,self-tuning and self-protection. The advantages of the design are 1) the overlayservices are in themselves self-managing, and sensor/actuation services they pro-vide are robust, 2) management can be dealt with declaratively and at a high-level,and 3) the overlay services provide good scalability in dynamic VOs.

  • 32.
    Carbone, Paris
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions2015Conference paper (Refereed)
    Abstract [en]

    The problem of automated personalised news recommendation, often referred as auto-scoring has attracted substantial research throughout the last decade in multiple domains such as data mining and machine learning, computer systems, e commerce and sociology. A typical "recommender systems" approach to solving this problem usually adopts content-based scoring, collaborative filtering or more often a hybrid approach. Due to their special nature, news articles introduce further challenges and constraints to conventional item recommendation problems, characterised by short lifetime and rapid popularity trends. In this survey, we provide an overview of the challenges and current solutions in news personalisation and ranking from both an algorithmic and system design perspective, and present our evaluation of the most representative scoring algorithms while also exploring the benefits of using a hybrid approach. Our evaluation is based on a real-life case study in news recommendations.

  • 33. Chen, J.
    et al.
    Lee, Y. C.
    Taufer, M.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Wu, X.
    Jin, H.
    Parashar, M.
    Yang, L. T.
    Message from BDCloud Chairs2015In: Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2015, p. xv-xvi, article id 7034744Conference paper (Refereed)
  • 34.
    Chikafa, Gibson
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Hopsworks.
    Sheikholeslami, Sina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Niazi, Salman
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Hopsworks.
    Dowling, Jim
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Cloud-native RStudio on Kubernetes for Hopsworks2023Manuscript (preprint) (Other academic)
    Abstract [en]

    In order to fully benefit from cloud computing, services are designed following the “multi-tenant” architectural model, which is aimed at maximizing resource sharing among users. However, multi-tenancy introduces challenges of security, performance isolation, scaling, and customization. RStudio server is an open-source Integrated Development Environment (IDE) accessible over a web browser for the R programming language. We present the design and implementation of a multi-user distributed system on Hopsworks, a data-intensive AI platform, following the multi-tenant model that provides RStudio as Software as a Service (SaaS). We use the most popular cloud-native technologies: Docker and Kubernetes, to solve the problems of performance isolation, security, and scaling that are present in a multi-tenant environment. We further enable secure data sharing in RStudio server instances to provide data privacy and allow collaboration among RStudio users. We integrate our system with Apache Spark, which can scale and handle Big Data processing workloads. Also, we provide a UI where users can provide custom configurations and have full control of their own RStudio server instances. Our system was tested on a Google Cloud Platform cluster with four worker nodes, each with 30GB of RAM allocated to them. The tests on this cluster showed that 44 RStudio servers, each with 2GB of RAM, can be run concurrently. Our system can scale out to potentially support hundreds of concurrently running RStudio servers by adding more resources (CPUs and RAM) to the cluster or system.

  • 35.
    Danniswara, Ken
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Peiro Sajjad, Hooman
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Stream Processing in Community Network Clouds2015In: Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, IEEE conference proceedings, 2015, p. 800-805Conference paper (Refereed)
    Abstract [en]

    Community Network Cloud is an emerging distributed cloud infrastructure that is built on top of a community network. The infrastructure consists of a number of geographically distributed compute and storage resources, contributed by community members, that are linked together through the community network. Stream processing is an important enabling technology that, if provided in a Community Network Cloud, would enable a new class of applications, such as social analysis, anomaly detection, and smart home power management. However, modern stream processing engines are designed to be used inside a data center, where servers communicate over a fast and reliable network. In this work, we evaluate the Apache Storm stream processing framework in an emulated Community Network Cloud in order to identify the challenges and bottlenecks that exist in the current implementation. The community network emulation was performed using data collected from the Guifi.net community network, Spain. Our evaluation results show that, with proper configuration of the heartbeats, it is possible to run Apache Storm in a Community Network Cloud. The performance is sensitive to the placement of the Storm components in the network. The deployment of management components on wellconnected nodes improves the Storm topology scheduling time, fault tolerance, and recovery time. Our evaluation also indicates that the Storm scheduler and the stream groupings need to be aware of the network topology and location of stream sources in order to optimally place Storm spouts and bolts to improve performance.

    Download full text (pdf)
    Stream Processing in Community Network Clouds
  • 36.
    de Palma, Noel
    et al.
    INRIA, France.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Parlavantzas, Nikos
    INRIA, Grenoble, France.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Tools for Architecture Based Autonomic Systems2009In: ICAS: 2009 Fifth International Conference on Autonomic and Autonomous Systems, IEEE Communications Society, 2009, p. 313-320Conference paper (Refereed)
    Abstract [en]

    Recent years have seen a growing interest in autonomic computing, an approach to providing systems with self managing properties. Autonomic computing aims to address the increasing complexity of the administration of large systems. The contribution of this paper is to provide a generic tool to ease the development of autonomic managers. Using this tool, an administrator provides a set of alternative architectures and specifies conditions that are used by autonomic managers to update architectures at runtime. Software changes are computed as architectural differences in terms of component model artifacts (components, attributes, bindings, etc.). These differences are then used to migrate into the next architecture by reconfiguring only the required part of the running system.

  • 37.
    Dhariwal, Sumeet
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. University of Oxford, Oxford, UK.
    Liu, Ying
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Clothing Classification using Unsupervised Pre-Training2020In: 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 82-89Conference paper (Refereed)
    Abstract [en]

    Deep Learning has changed the way computer vision tasks are being solved in recent times. Deep Learning based approaches have achieved outstanding results in computer vision tasks including image classification, segmentation, object detection. Most of this success has been achieved by training deep neural networks on labelled data. In general, the more labelled data is fed to a deep learning model, the more accurate the model will be. However, labelling is time consuming and sometimes even impossible.Fashion and e-commerce are domains where a large amount of unlabelled data is available. There is a huge need to leverage these data without labels. The aim of this paper is to explore and evaluate the possibility and effectiveness of using massive amount of unlabelled data to build deep learning models. We compare the performance of these models with the performance of models built with labelled data. Specifically, we compare fully supervised deep learning with two deep learning methods with unsupervised pre-training. Our pre-trainings are based on clustering of features called DeepCluster and rotation as a self-supervision task. The comparison is performed on the DeepFashion dataset.Our experimental results have shown that using unsupervised pre-training can attain comparable classification accuracy (~1-4 % difference) on image classification comparing to fully supervised models. Furthermore, we have shown that our models uses five times less labelled data during the fine-tuning phase and still achieves comparable accuracy (~3-4 % difference) comparing to fully supervised models. These results demonstrate the potential of using unsupervised pre-training approaches in achieving comparable results to fully supervised models.

  • 38.
    Doroshenko, Anatoly
    et al.
    Institute of Software Systems, National Academy of Sciences of Ukraine, Kiev 252187, Ukraine.
    Thorelli, Lars-Erik
    KTH, Superseded Departments (pre-2005), Teleinformatics.
    Vlassov, Vladimir
    KTH, Superseded Departments (pre-2005), Teleinformatics.
    Coordination models and facilities could be parallel software accelerators1999In: HIGH-PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS, Berlin: Springer Berlin/Heidelberg, 1999, p. 1219-1222Conference paper (Refereed)
    Abstract [en]

     A  new  coordination  model  is  constructed  for  distributed shared  memory parallel programs.  It  exploits typing of shared resources and  formal specification of a  priori known  synchronization constraints.

  • 39.
    Fedeli, Stefano
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Schain, Frida
    Schain Res, Stockholm, Sweden..
    Imtiaz, Sana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Abbas, Zainab
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Privacy Preserving Survival Prediction2021In: 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) / [ed] Chen, Y Ludwig, H Tu, Y Fayyad, U Zhu, X Hu, X Byna, S Liu, X Zhang, J Pan, S Papalexakis, V Wang, J Cuzzocrea, A Ordonez, C, Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 4600-4608Conference paper (Refereed)
    Abstract [en]

    Predictive modeling has the potential to improve risk stratification of cancer patients and thereby contribute to optimized treatment strategies and better outcomes for patients in clinical practice. To develop robust predictive models for decision-making in healthcare, sensitive patient-level data is often required when developing the training models. Consequently, data privacy is an important aspect to consider when building these predictive models and in subsequent communication of the results. In this study we have used Graph Neural Networks for survival prediction, and compared the accuracy to state-of-the-art prediction models after applying Differential Privacy and k-Anonymity, i.e. two privacy-preservation solutions. By using two different data sources we demonstrated that Graph Neural Networks and Survival Forests are the two most well-performing survival prediction methods when used in combination with privacy preservation solutions. Furthermore, when the predictive model was built using clinical expertise in the specific area of interest, the prediction accuracy of the proposed knowledge based graph model drops by at most 10% when used with privacy preservation solutions. Our proposed knowledge based graph is therefore more suitable to be used in combination with privacy preservation solutions as compared to other graph models.

  • 40.
    Garcia Lozano, Marianela
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Brynielsson, Joel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Franke, Ulrik
    RISE Res Inst Sweden, POB 1263, SE-16429 Kista, Sweden..
    Rosell, Magnus
    FOI Swedish Def Res Agcy, SE-16490 Stockholm, Sweden..
    Tjörnhammar, Edward
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Varga, Stefan
    KTH. Swedish Armed Forces Headquarters, Stockholm, SE-107 85, Sweden.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Veracity assessment of online data2020In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 129, article id 113132Article in journal (Refereed)
    Abstract [en]

    Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research. The most common veracity assessment method among the studied set of papers is to perform text analysis using supervised learning. Regarding methods for machine learning much has happened in the last couple of years related to the advancements made in deep learning. However, very few papers make use of these advancements. Also, the papers in general tend to have a narrow scope, as they focus on solving a small task with only one type of data from one main source. The overall veracity assessment problem is complex, requiring a combination of data sources, data types, indicators, and methods. Only a few papers take on such a broad scope, thus, demonstrating the relative immaturity of the veracity assessment domain.

  • 41.
    Garcia Lozano, Marianela
    et al.
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Franke, Ulrik
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Rosell, Magnus
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Towards Automatic Veracity Assessment of Open Source Information2015In: 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015, p. 199-206Conference paper (Refereed)
    Abstract [en]

    Intelligence analysis is dependent on veracity assessment of Open Source Information (OSINF) which includes assessment of the reliability of sources and credibility of information. Traditionally, OSINF veracity assessment is done by intelligence analysts manually, but the large volumes, high velocity, and variety make it infeasible to continue doing so, and calls for automation. Based on meetings, interviews and questionnaires with military personnel, analysis of related work and state of the art, we identify the challenges and propose an approach and a corresponding framework for automated veracity assessment of OSINF. The framework provides a basis for new tools which will give the intelligence analysts the ability to automatically or semi-automatically assess veracity of larger amounts of data in a shorter amount of time. Instead of spending their time working with irrelevant, ambiguous, contradicting, biased, or plain wrong data, they can spend more time on analysis.

  • 42.
    Groleau, William
    et al.
    Institut National des Sciences Appliquees de Lyon (INSA), Lyon, France.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Towards Semantics-Based Resource Discovery for the Grid2007In: Integrated Research in GRID Computing: CoreGRID Integration Workshop 2005 (Selected Papers) November 28–30, Pisa, Italy / [ed] Sergei Gorlatch and Marco Danelutto, Springer-Verlag New York, 2007, p. 175-187Chapter in book (Refereed)
    Abstract [en]

     We present our experience and evaluation of some of the state-of-the-art  software tools  and algorithms  available  for building  a system  for  Grid  service  provision and discovery  using agents, ontologies  and semantic markups.  We believe that semantic information  will be used in every large-scale Grid resource  discovery, and the Grid should capitalize on existing research and development  in the area. We built a prototype of an agent-based system for resource provision and selection that  allows  locating  services  that  semantically  match  the  client  requirements. Services are described using the Web service ontology (OWL-S). We present our prototype built on the JADE agent framework and an off-the-shelf  OWL-S toolkit. We also present preliminary evaluation results, which in particular indicate a need for  an incremental  classification  algorithm  supporting  incremental  extension of a knowledge base with many unrelated or weakly-related  ontologies.

  • 43.
    Guo, Yao
    et al.
    School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Ashok, Raksit
    Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.
    Weiss, Richard
    The Evergreen State College, Olympia, WA 98505, USA.
    Andras Moritz, Csaba
    Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA.
    Synchronization coherence: A transparent hardware mechanism for cache coherence and fine-grained synchronization2008In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 68, no 2, p. 165-181Article in journal (Refereed)
    Abstract [en]

    The quest to improve performance forces designers to explore finer-grained multiprocessor machines. Ever increasing chip densities based on CMOS improvements fuel research in highly parallel chip multiprocessors with 100s of processing elements. With such increasing levels of parallelism, synchronization is set to become a major performance bottleneck and efficient support for synchronization an important design criterion. Previous research has shown that integrating support for fine-grained synchronization can have significant performance benefits compared to traditional coarse-grained synchronization. Not much progress has been made in supporting fine-grained synchronization transparently to processor nodes: a key reason perhaps why wide adoption has not followed. In this paper, we propose a novel approach called synchronization coherence that can provide transparent fine-grained synchronization and caching in a multiprocessor machine and single-chip multiprocessor. Our approach merges fine-grained synchronization mechanisms with traditional cache coherence protocols. It reduces network utilization as well as synchronization related processing overheads while adding minimal hardware complexity as compared to cache coherence mechanisms or previously reported fine-grained synchronization techniques. In addition to its benefit of making synchronization transparent to processor nodes, for the applications studied, it provides up to 23% improvement in performance and up to 24% improvement in energy efficiency with no L2 caches compared to previous fine-grained synchronization techniques. The performance improvement increases up to 38% when simulating with an ideal L2 cache system.

  • 44. Gureya, D.
    et al.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Barreto, J.
    Brief announcement: BALM: qos-aware memory bandwidth partitioning for multi-socket cloud nodes2021In: Annual ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery (ACM) , 2021, p. 435-438Conference paper (Refereed)
    Abstract [en]

    The recent emergence of novel hardware-based resource partitioning mechanisms has unveiled the opportunity for a new generation of QoS-aware resource allocation approaches for workload consolidation. Still, to the best of our knowledge, existing proposals are, by design, not tailored to the growing prevalence of multi-socket systems in contemporary warehouse-scale data centers. We propose BALM, a QoS-aware memory bandwidth allocation technique for multi-socket architectures that combines commodity bandwidth allocation mechanisms with a novel adaptive cross-socket page migration scheme. Our experimental evaluation with real applications on a dual-socket machine shows that BALM can overcome the efficiency limitations of state-of-the-art. BALM can ensure marginal SLO violation windows while delivering up to 87% throughput gains to bandwidth-intensive best-effort applications when compared to state-of-the-art alternatives.

  • 45.
    Gureya, David
    et al.
    KTH. Univ Lisbon, INIESC ID, Lisbon, Portugal..
    Barreto, Joao
    Univ Lisbon, INIESC ID, Lisbon, Portugal..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Generalizing QoS-Aware Memory Bandwidth Allocation to Multi-Socket Cloud Servers2021In: 2021 Ieee 14Th International Conference On Cloud Computing (Cloud 2021) / [ed] Ardagna, CA Chang, C Daminai, E Ranjan, R Wang, Z Ward, R Zhang, J Zhang, W, Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 551-557Conference paper (Refereed)
    Abstract [en]

    Although the problem of QoS-aware resource allocation is not new, novel hardware-based resource allocation mechanisms have recently become available in commodity cloud servers and enabled a new generation of QoS-aware resource allocation approaches. Unfortunately, to the best of our knowledge, existing proposals are by design tailored to single-socket architectures only. In many warehouse scale data centers, dual-socket (or even larger) machines already constitute the largest share of hosts. This paper presents the full design and implementation of BALM, a QoS-aware memory bandwidth allocation technique for multi-socket architectures. BALM combines commodity bandwidth allocation mechanisms originally designed for single-socket with a novel adaptive cross-socket page migration scheme. Our evaluation with a large and dynamic set of real applications co-located on a dual-socket machine shows that BALM can overcome the efficiency limitations of state-of-the-art. BALM delivers substantial throughput gains to bandwidth-intensive best-effort applications, while ensuring marginal SID violation windows to latency-critical applications.

  • 46.
    Gureya, David Daharewa
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Neto, João
    INESC-ID, Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal.
    Karimi, Reza
    Emory University, Atlanta, GA, USA.
    Barreto, João
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    Bhatotia, Pramod
    University of Edinburgh, Edinburgh, United Kingdom.
    Quema, Vivien
    Grenoble INP/ENSIMAG, Grenoble, France.
    Rodrigues, Rodrigo
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    Romano, Paolo
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Bandwidth-Aware Page Placement in NUMA2020In: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 546-556Conference paper (Refereed)
    Abstract [en]

    Page placement is a critical problem for memory-intensive applications running on a shared-memory multiprocessor with a non-uniform memory access (NUMA) architecture. State-of-the-art page placement mechanisms interleave pages evenly across NUMA nodes. However, this approach fails to maximize memory throughput in modern NUMA systems, characterized by asymmetric bandwidths and latencies, and sensitive to memory contention and interconnect congestion phenomena.We propose BWAP, a novel page placement mechanism based on asymmetric weighted page interleaving. BWAP combines an analytical performance model of the target NUMA system with on-line iterative tuning of page distribution for a given memory-intensive application. Our experimental evaluation with representative memory-intensive workloads shows that BWAP performs up to 66% better than state-of-the-art techniques. These gains are particularly relevant when multiple co-located applications run in disjoint partitions of a large NUMA machine or when applications do not scale up to the total number of cores.

  • 47.
    Hagos, Desta Haileselassie
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Kakantousis, Theofilos
    Logical Clocks AB, S-11872 Stockholm, Sweden..
    Sheikholeslami, Sina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Wang, Tianze
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Payberah, Amir Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Meister, Moritz
    Logical Clocks AB, S-11872 Stockholm, Sweden..
    Andersson, Robin
    Logical Clocks AB, S-11872 Stockholm, Sweden..
    Dowling, Jim
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Logical Clocks AB, S-11872 Stockholm, Sweden.
    Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks2022In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 8, article id 1889Article in journal (Refereed)
    Abstract [en]

    This paper introduces the Hopsworks platform to the entire Earth Observation (EO) data community and the Copernicus programme. Hopsworks is a scalable data-intensive open-source Artificial Intelligence (AI) platform that was jointly developed by Logical Clocks and the KTH Royal Institute of Technology for building end-to-end Machine Learning (ML)/Deep Learning (DL) pipelines for EO data. It provides the full stack of services needed to manage the entire life cycle of data in ML. In particular, Hopsworks supports the development of horizontally scalable DL applications in notebooks and the operation of workflows to support those applications, including parallel data processing, model training, and model deployment at scale. To the best of our knowledge, this is the first work that demonstrates the services and features of the Hopsworks platform, which provide users with the means to build scalable end-to-end ML/DL pipelines for EO data, as well as support for the discovery and search for EO metadata. This paper serves as a demonstration and walkthrough of the stages of building a production-level model that includes data ingestion, data preparation, feature extraction, model training, model serving, and monitoring. To this end, we provide a practical example that demonstrates the aforementioned stages with real-world EO data and includes source code that implements the functionality of the platform. We also perform an experimental evaluation of two frameworks built on top of Hopsworks, namely Maggy and AutoAblation. We show that using Maggy for hyperparameter tuning results in roughly half the wall-clock time required to execute the same number of hyperparameter tuning trials using Spark while providing linear scalability as more workers are added. Furthermore, we demonstrate how AutoAblation facilitates the definition of ablation studies and enables the asynchronous parallel execution of ablation trials.

  • 48.
    Hagos, Desta Haileselassie
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Kakantousis, Theofilos
    Log Clocks, S-16440 Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Sheikholeslami, Sina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Wang, Tianze
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Dowling, Jim
    Log Clocks, S-16440 Stockholm, Sweden..
    Paris, Claudia
    Univ Trento, I-38122 Trento, Italy..
    Marinelli, Daniele
    Univ Trento, I-38122 Trento, Italy..
    Weikmann, Giulio
    Univ Trento, I-38122 Trento, Italy..
    Bruzzone, Lorenzo
    Univ Trento, I-38122 Trento, Italy..
    Khaleghian, Salman
    UiT Arctic Univ Norway, N-9019 Tromso, Norway..
    Kraemer, Thomas
    UiT Arctic Univ Norway, N-9019 Tromso, Norway..
    Eltoft, Torbjorn
    UiT Arctic Univ Norway, N-9019 Tromso, Norway..
    Marinoni, Andrea
    UiT Arctic Univ Norway, N-9019 Tromso, Norway..
    Pantazi, Despina-Athanasia
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Stamoulis, George
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Bilidas, Dimitris
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Papadakis, George
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Mandilaras, George
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Koubarakis, Manolis
    Natl & Kapodistrian Univ Athens, Athens 15772, Greece..
    Troumpoukis, Antonis
    Natl Ctr Sci Res Demokritos, Paraskevi 15341, Greece..
    Konstantopoulos, Stasinos
    Natl Ctr Sci Res Demokritos, Paraskevi 15341, Greece..
    Muerth, Markus
    VISTA Remote Sensing Geosci GmbH, D-80333 Munich, Germany..
    Appel, Florian
    VISTA Remote Sensing Geosci GmbH, D-80333 Munich, Germany..
    Fleming, Andrew
    British Antarctic Survey, Cambridge CB3 0ET, England..
    Cziferszky, Andreas
    British Antarctic Survey, Cambridge CB3 0ET, England..
    ExtremeEarth Meets Satellite Data From Space2021In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 14, p. 9038-9063Article in journal (Refereed)
    Abstract [en]

    Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs.

  • 49.
    Imtiaz, Sana
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Catholic Univ Louvain, Louvain La Neuve, Belgium..
    Arsalan, Muhammad
    Otto von Guericke Univ, Magdeburg, Germany..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Sadre, Ramin
    Catholic Univ Louvain, Louvain La Neuve, Belgium..
    Synthetic and Private Smart Health Care Data Generation using GANs2021In: 30th International Conference on Computer Communications and Networks (ICCCN 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper (Refereed)
    Abstract [en]

    With the rapid advancements in machine learning, the health care paradigm is shifting from treatment towards prevention. The smart health care industry relies on the availability of large-scale health datasets in order to benefit from machine learning-based services. As a consequence, preserving the individuals' privacy becomes vital for sharing sensitive personal information. Synthetic datasets with generative models are considered to be one of the most promising solutions for privacy-preserving data sharing. Among the generative models, generative adversarial networks (GANs) have emerged as the most impressive models for synthetic data generation in recent times. However, smart health care data is attributed with unique challenges such as volume, velocity, and various data types and distributions. We propose a GAN coupled with differential privacy mechanisms for generating a realistic and private smart health care dataset. The proposed approach is not only able to generate realistic synthetic data samples but also the differentially private data samples under different settings: learning from a noisy distribution or noising the learned distribution. We tested and evaluated our proposed approach using a real-world Fitbit dataset. Our results indicate that our proposed approach is able to generate quality synthetic and differentially private dataset that preserves the statistical properties of the original dataset.

  • 50.
    Imtiaz, Sana
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Horchidan, Sonia-Florina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Abbas, Zainab
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Arsalan, Muhammad
    Otto-von-Guericke Universitat Magdeburg, Magdeburg, Germany.
    Chaudhry, Hassan Nazeer
    DEIB, Politecnico di Milano, Milan, Italy.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Privacy Preserving Time-Series Forecasting of User Health Data Streams2020In: 2020 IEEE International Conference on Big Data (Big Data), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 3428-3437Conference paper (Refereed)
    Abstract [en]

    Privacy preservation plays a vital role in health care applications as the requirements for privacy preservation are very strict in this domain. With the rapid increase in the amount, quality and detail of health data being gathered with smart devices, new mechanisms are required that can cope with the challenges of large scale and real-time processing requirements. Federated learning (FL) is one of the conventional approaches that facilitate the training of AI models without access to the raw data. However, recent studies have shown that FL alone does not guarantee sufficient privacy. Differential privacy (DP) is a well-known approach for privacy guarantees, however, because of the noise addition, DP needs to make a trade-off between privacy and accuracy. In this work, we design and implement an end-to-end pipeline using DP and FL for the first time in the context of health data streams. We propose a clustering mechanism to leverage the similarities between users to improve the prediction accuracy as well as significantly reduce the model training time. Depending on the dataset and features, our predictions are no more than 0.025% far off the ground-truth value with respect to the range of value. Moreover, our clustering mechanism brings a significant reduction in the training time, with up to 49% reduction in prediction accuracy error in the best case, as compared to training a single model on the entire dataset. Our proposed privacy preserving mechanism at best introduces a decrease of ≈ 2% in the prediction accuracy of the trained models. Furthermore, our proposed clustering mechanism reduces the prediction error even in highly noisy settings by as much as 38% as compared to using a single federated private model.

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