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  • 1.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS. RISE SICS, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks2018Konferensbidrag (Refereegranskat)
    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, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Kalavri, Vasiliki
    Systems Group, ETH, Zurich, Switzerland.
    Carbone, Paris
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Streaming Graph Partitioning: An Experimental Study2018Ingår i: Proceedings of the VLDB Endowment, ISSN 2150-8097, E-ISSN 2150-8097, Vol. 11, nr 11, s. 1590-1603Artikel i tidskrift (Refereegranskat)
    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.

  • 3.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Sigurdsson, Thorsteinn Thorri
    KTH.
    Al-Shishtawy, Ahmad
    RISE Res Inst Sweden, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection2018Ingår i: 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, s. 1017-1025Konferensbidrag (Refereegranskat)
    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.

  • 4.
    Ahlberg, Michael
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Yasui, Terumasa
    Advanced Technology Research and Development Center, Mitsubishi Electric Corporation, Amagasaki, Japan.
    Router placement in wireless sensor networks2006Ingår i: 2006 IEEE International Conference on Mobile Adhoc and Sensor Systems, Vols 1 and 2, IEEE , 2006, s. 498-501Konferensbidrag (Refereegranskat)
    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.

  • 5.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Asif Fayyaz, Muhammad
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Popov, Konstantin
    Swedish Institute of Computer Science.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Achieving robust self-management for large-scale distributed applications2010Rapport (Övrig (populärvetenskap, debatt, mm))
    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.

     

  • 6.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Bao, Lin
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Policy based self-management in distributed environments2010Ingår i: 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop (SASOW), IEEE Computer Society Digital Library, 2010, s. 256-260Konferensbidrag (Refereegranskat)
    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.

  • 7.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Fayyaz, Muhammad Asif
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Achieving Robust Self-Management for Large-Scale Distributed Applications2010Ingår i: Self-Adaptive and Self-Organizing Systems (SASO), 2010 4th IEEE International Conference on: SASO 2010, IEEE Computer Society, 2010, s. 31-40Konferensbidrag (Refereegranskat)
    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.

  • 8.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, 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, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Distributed Control Loop Patterns for Managing Distributed Applications2008Ingår i: SASOW 2008: SECOND IEEE INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS, PROCEEDINGS / [ed] Serugendo GD, LOS ALAMITOS: IEEE Computer Society, 2008, s. 260-265Konferensbidrag (Refereegranskat)
    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.

  • 9.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, 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, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Enabling Self-Management Of Component Based Distributed Applications2008Ingår i: FROM GRIDS TO SERVICE AND PERVASIVE COMPUTING, Springer-Verlag New York, 2008, s. 163-174Konferensbidrag (Refereegranskat)
    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.

  • 10.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Khan, Tareq Jamal
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Robust Fault-Tolerant Majority-Based Key-Value Store Supporting Multiple Consistency Levels2011Ingår i: 2011 IEEE 17TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2011, s. 589-596Konferensbidrag (Refereegranskat)
    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.

  • 11.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    ElastMan: Autonomic elasticity manager for cloud-based key-value stores2013Ingår i: HPDC 2013 - Proceedings of the 22nd ACM International Symposium on High-Performance Parallel and Distributed Computing, 2013, s. 115-116Konferensbidrag (Refereegranskat)
    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.

  • 12.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    ElastMan: Autonomic Elasticity Manager for Cloud-Based Key-Value Stores2012Rapport (Övrigt vetenskapligt)
    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.

  • 13.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    ElastMan: Elasticity manager for elastic key-value stores in the cloud2013Ingår i: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, New York, NY, USA: Association for Computing Machinery (ACM), 2013, s. 7:1-7:10Konferensbidrag (Refereegranskat)
    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.

  • 14.
    Al-Shishtawy, Ahmad
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Brand, Per
    Swedish Institute of Computer Science.
    Haridi, Seif
    Swedish Institute of Computer Science.
    A design methodology for self-management in distributed environments2009Ingår i: IEEE International conference on Computational Science and Engineering, 2009, s. 430-436Konferensbidrag (Refereegranskat)
    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.

  • 15.
    Arman, Ala
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Al-Shishtawy, Ahmad
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Elasticity controller for Cloud-based key-value stores2012Ingår i: Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, IEEE , 2012, s. 268-275Konferensbidrag (Refereegranskat)
    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.

  • 16.
    Awan, Ahsan Javed
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brorsson, Mats
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Architectural Impact on Performance of In-memoryData Analytics: Apache Spark Case StudyManuskript (preprint) (Övrigt vetenskapligt)
    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

  • 17.
    Awan, Ahsan Javed
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brorsson, Mats
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ayguade, Eduard
    Technical University of Catalunya, Barcelona Super Computing Center.
    How Data Volume Affects Spark Based Data Analytics on a Scale-up Server2015Ingår i: 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, s. 81-92Konferensbidrag (Refereegranskat)
    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.

  • 18.
    Awan, Ahsan Javed
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brorsson, Mats
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Micro-architectural Characterization of Apache Spark on Batch and Stream Processing Workloads2016Konferensbidrag (Refereegranskat)
    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.

  • 19.
    Awan, Ahsan Javed
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brorsson, Mats
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ayguade, Eduard
    Barcelona Super Computing Center and Technical University of Catalunya.
    Node architecture implications for in-memory data analytics on scale-in clusters2016Konferensbidrag (Refereegranskat)
    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.

  • 20.
    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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Zennaro, Marco
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    The Cloudy Distribution in Community Network Clouds in Guifi.net2015Konferensbidrag (Refereegranskat)
    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.

  • 21.
    Baig, Roger
    et al.
    Fundacio Privada per la Xarxa Lliure, Oberta i Neural Guifi.net. Mas l’Esperanca, 08503 Gurb, Catalonia.
    Dowling, Jim
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, 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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Zennaro, Marco
    The Abdus Salam International Centre for Theoretical Physics (ICTP). Trieste, Italy.
    Deploying Clouds in the Guifi Community Network2015Ingår i: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, IEEE , 2015, s. 1020-1025Konferensbidrag (Refereegranskat)
    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.

  • 22.
    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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Community clouds at the edge deployed in Guifi.net2015Konferensbidrag (Refereegranskat)
    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.

  • 23.
    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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Cloud-based community services in community networks2016Ingår i: 2016 International Conference on Computing, Networking and Communications, ICNC 2016, IEEE conference proceedings, 2016, s. 1-5, artikel-id 7440621Konferensbidrag (Refereegranskat)
    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.

  • 24.
    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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Community network clouds as a case for the IEEE Intercloud standardization2015Ingår i: 2015 IEEE Conference on Standards for Communications and Networking, CSCN 2015, 2015, s. 269-274, artikel-id 7390456Konferensbidrag (Refereegranskat)
    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.

  • 25. Baig, Roger
    et al.
    Freitag, Felix
    Moll, Agusti
    Navarro, Leandro
    Pueyo, Roger
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Experiences in Building Micro-Cloud Provider Federation in the Guifi Community Network2015Ingår i: 2015 IEEE/ACM 8TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2015, s. 516-521Konferensbidrag (Refereegranskat)
    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.

  • 26.
    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, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Al-Shishtawy, Ahmad
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    The Role of Overlay Services In a Self-Managing Framework for Dynamic Virtual Organizations2008Ingår i: 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, s. 153-164Konferensbidrag (Refereegranskat)
    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.

  • 27.
    Carbone, Paris
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions2015Konferensbidrag (Refereegranskat)
    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.

  • 28. Chen, J.
    et al.
    Lee, Y. C.
    Taufer, M.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Wu, X.
    Jin, H.
    Parashar, M.
    Yang, L. T.
    Message from BDCloud Chairs2015Ingår i: 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, s. xv-xvi, artikel-id 7034744Konferensbidrag (Refereegranskat)
  • 29.
    Danniswara, Ken
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Peiro Sajjad, Hooman
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Stream Processing in Community Network Clouds2015Ingår i: Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, IEEE conference proceedings, 2015, s. 800-805Konferensbidrag (Refereegranskat)
    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.

  • 30.
    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, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Tools for Architecture Based Autonomic Systems2009Ingår i: ICAS: 2009 Fifth International Conference on Autonomic and Autonomous Systems, IEEE Communications Society, 2009, s. 313-320Konferensbidrag (Refereegranskat)
    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.

  • 31.
    Doroshenko, Anatoly
    et al.
    Institute of Software Systems, National Academy of Sciences of Ukraine, Kiev 252187, Ukraine.
    Thorelli, Lars-Erik
    KTH, Tidigare Institutioner, Teleinformatik.
    Vlassov, Vladimir
    KTH, Tidigare Institutioner, Teleinformatik.
    Coordination models and facilities could be parallel software accelerators1999Ingår i: HIGH-PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS, Berlin: Springer Berlin/Heidelberg, 1999, s. 1219-1222Konferensbidrag (Refereegranskat)
    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.

  • 32.
    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, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Towards Automatic Veracity Assessment of Open Source Information2015Ingår i: 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015, s. 199-206Konferensbidrag (Refereegranskat)
    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.

  • 33.
    Groleau, William
    et al.
    Institut National des Sciences Appliquees de Lyon (INSA), Lyon, France.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Popov, Konstantin
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    Towards Semantics-Based Resource Discovery for the Grid2007Ingår i: 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, s. 175-187Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 34.
    Guo, Yao
    et al.
    School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, 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 synchronization2008Ingår i: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 68, nr 2, s. 165-181Artikel i tidskrift (Refereegranskat)
    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.

  • 35.
    Javed Awan, Ahsan
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brorsson, Mats
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ayguade, Eduard
    Technical University of Catalunya (UPC), Computer Architecture Department.
    Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server2015Ingår i: Proceedings - 2015 IEEE 5th International Conference on Big Data and Cloud Computing, BDCloud 2015, IEEE Computer Society, 2015, s. 1-8, artikel-id 7310708Konferensbidrag (Refereegranskat)
    Abstract [en]

    In last decade, data analytics have rapidly progressed from traditional disk-based processing tomodern in-memory processing. However, little effort has been devoted at enhancing performance at micro-architecture level. This paper characterizes the performance of in-memory data analytics using Apache Spark framework. We use a single node NUMA machine and identify the bottlenecks hampering the scalability of workloads. We also quantify the inefficiencies at micro-architecture level for various data analysis workloads. Through empirical evaluation, we show that spark workloads do not scale linearly beyond twelve threads, due to work time inflation and thread level load imbalance. Further, at the micro-architecture level, we observe memory bound latency to be the major cause of work time inflation.

  • 36.
    Jernberg, Jimmy
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Ghodsi, Ali
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Haridi, Seif
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    DOH: A Content Delivery Peer-to-Peer Network2006Ingår i: Euro-Par 2006 Parallel Processing: 12th International Euro-Par Conference, Dresden, Germany, August 28 – September 1, 2006. Proceedings / [ed] Wolfgang E. Nagel, Wolfgang V. Walter and Wolfgang Lehner, 2006, Vol. 4128, s. 1026-1039Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many SMEs and non-profit organizations suffer when their Web servers become unavailable due to flash crowd effects when their web site becomes popular. One of the solutions to the flash-crowd problem is to place the web site on a scalable CDN (Content Delivery Network) that replicates the content and distributes the load in order to improve its response time. In this paper, we present our approach to building a scalable Web Hosting environment as a CDN on top of a structured peer-to-peer system of collaborative web-servers integrated to share the load and to improve the overall system performance, scalability, availability and robustness. Unlike cluster-based solutions, it can run on heterogeneous hardware, over geographically dispersed areas. To validate and evaluate our approach, we have developed a system prototype called DOH (DKS Organized Hosting) that is a CDN implemented on top of the DKS (Distributed K-nary Search) structured P2P system with DHT (Distributed Hash table) functionality [9]. The prototype is implemented in Java, using the DKS middleware, the Jetty web-server, and a modified JavaFTP server. The proposed design of CDN has been evaluated by simulation and by evaluation experiments on the prototype.

  • 37. Jimenez, J.
    et al.
    Baig, R.
    Escrich, P.
    Khan, A. M.
    Freitag, F.
    Navarro, L.
    Pietrosemoli, E.
    Zennaro, M.
    Payberah, A. H.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Supporting cloud deployment in the Guifi.net community network2013Ingår i: 2013 Global Information Infrastructure Symposium, IEEE , 2013, s. 6684361-Konferensbidrag (Refereegranskat)
    Abstract [en]

    Community networking is an emerging model of a shared communication infrastructure in which communities of citizens build and own open networks. Community networks offer successfully IP-based networking to the user. Cloud computing infrastructures however, while common in today's Internet, hardy exist in community networks. We explain our approach to bring clouds into the Guifi.net community network. For this we have started integrating part of our cloud prototype into the Guifi.net community network management tools. A proof-of-concept cloud infrastructure is currently under deployment in the Guifi.net community network. Our long term vision is that the users of community networks will not need to consume cloud applications from the Internet, but find them within the community network.

  • 38.
    Kalavri, Vasiliki
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brundza, Vaidas
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Block Sampling: Efficient Accurate Online Aggregation in MapReduce2013Ingår i: Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, 2013, s. 250-257Konferensbidrag (Refereegranskat)
    Abstract [en]

    Large-scale data processing frameworks, such as Hadoop MapReduce, are widely used to analyze enormous amounts of data. However, processing is often time-consuming, preventing interactive analysis. One way to decrease response time is partial job execution, where an approximate, early result becomes available to the user, prior to job completion. The Hadoop Online Prototype (HOP) uses online aggregation to provide early results, by partially executing jobs on subsets of the input, using a simplistic progress metric. Due to its sequential nature, values are not objectively represented in the input subset, often resulting in poor approximations or "data bias". In this paper, we propose a block sampling technique for large-scale data processing, which can be used for fast and accurate partial job execution. Our implementation of the technique on top of HOP uniformly samples HDFS blocks and uses in-memory shuffling to reduce data bias. Our prototype significantly improves the accuracy of HOP's early results, while only introducing minimal overhead. We evaluate our technique using real-world datasets and applications and demonstrate that our system outperforms HOP in terms of accuracy. In particular, when estimating the average temperature of the studied dataset, our system provides high accuracy (less than 20% absolute error) after processing only 10% of the input, while HOP needs to process 70% of the input to yield comparable results.

  • 39.
    Kalavri, Vasiliki
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Ewen, Stephan
    TU Berlin.
    Tzoumas, Kostas
    TU Berlin.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Markl, Volker
    TU Berlin.
    Haridi, Seif
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Asymmetry in Large-Scale Graph Analysis, Explained2014Ingår i: Proceedings of the Second International Workshop on Graph Data ManagementExperience and Systems (GRADES 2014), June 22, 2014, Snowbird, Utah, USA., 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Iterative computations are in the core of large-scale graph processing. In these applications, a set of parameters is continuously refined, until a fixed point is reached. Such fixed point iterations often exhibit non-uniform computational behavior, where changes propagate with different speeds throughout the parameter set, making them active or inactive during iterations. This asymmetrical behavior can lead to a many redundant computations, if not exploited. Many specialized graph processing systems and APIs exist that run iterative algorithms efficiently exploiting this asymmetry. However, their functionality is sometimes vaguely defined and due to their different programming models and terminology used, it is often challenging to derive equivalence between them. We describe an optimization framework for iterative graph processing, which utilizes dataset dependencies. We explain several optimization techniques that exploit asymmetrical behavior of graph algorithms. We formally specify the conditions under which, an algorithm can use a certain technique. We also design template execution plans, using a canonical set of dataflow operators and we evaluate them using real-world datasets and applications. Our experiments show that optimized plans can significantly reduce execution time, often by an order of magnitude. Based on our experiments, we identify a trade-off that can be easily captured and could serve as the basis for automatic optimization of large-scale graph-processing applications.

  • 40.
    Kalavri, Vasiliki
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Shang, Hui
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    m2r2: A Framework for Results Materialization and Reuse in High-Level Dataflow Systems for Big Data2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    High-level parallel dataflow systems, such as Pig and Hive, have lately gained great popularity in the area of big data processing. These systems often consist of a declarative query language and a set of compilers, which transform queries into execution plans and submit them to a distributed engine for execution. Apart from the useful abstraction and support for common analysis operations, high-level processing systems also offer great opportunities for automatic optimizations. Existing studies on execution traces from big data centers and industrial clusters show that there is significant computation redundancy in analysis programs, i.e., there exist similar or even identical queries on the same datasets in different jobs. Furthermore, workload characterization of MapReduce traces from large organizations suggest that there is a big need for caching job results, that will enable their reuse and improve execution time. In this paper, we propose m2r2, an extensible and language-independent framework for results materialization and reuse in high-level dataflow systems for big data analytics. Our prototype implementation is built on top of the Pig dataflow system and handles automatic results caching, common sub-query matching and rewriting, as well as garbage collection. We have evaluated m2r2 using the TPC-H benchmark for Pig and report reduced query execution time by 65% on average.

  • 41.
    Kalavri, Vasiliki
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    MapReduce: Limitations, optimizations and open issues2013Ingår i: Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, IEEE , 2013, s. 1031-1038Konferensbidrag (Refereegranskat)
    Abstract [en]

    MapReduce has recently gained great popularity as a programming model for processing and analyzing massive data sets and is extensively used by academia and industry. Several implementations of the MapReduce model have emerged, the Apache Hadoop framework being the most widely adopted. Hadoop offers various utilities, such as a distributed file system, job scheduling and resource management capabilities and a Java API for writing applications. Hadoop's success has intrigued research interest and has led to various modifications and extensions to the framework. Implemented optimizations include performance improvements, programming model extensions, tuning automation and usability enhancements. In this paper, we discuss the current state of the Hadoop framework and its identified limitations. We present, compare and classify Hadoop/MapReduce variations, identify trends, open issues and possible future directions.

  • 42.
    Kalavri, Vasiliki
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Brand, Per
    Swedish Institute of Computer Science (SICS), Kista, Sweden.
    PonIC: Using Stratosphere to Speed Up Pig Analytics2013Ingår i: Euro-Par 2013 Parallel Processing: 19th International Conference, Aachen, Germany, August 26-30, 2013. Proceedings, Springer Berlin/Heidelberg, 2013, s. 279-290Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pig, a high-level dataflow system built on top of Hadoop MapReduce, has greatly facilitated the implementation of data-intensive applications. Pig successfully manages to conceal Hadoop’s one input and two-stage inflexible pipeline limitations, by translating scripts into MapReduce jobs. However, these limitations are still present in the backend, often resulting in inefficient execution.Stratosphere, a data-parallel computing framework consisting of PACT, an extension to the MapReduce programming model and the Nephele execution engine, overcomes several limitations of Hadoop MapReduce. In this paper, we argue that Pig can highly benefit from using Stratosphere as the backend system and gain performance, without any loss of expressiveness.We have ported Pig on top of Stratosphere and we present a process for translating Pig Latin scripts into PACT programs. Our evaluation shows that Pig Latin scripts can execute on our prototype up to 8 times faster for a certain class of applications.

  • 43. Kalavri, Vasiliki
    et al.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Haridi, Seif
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    High-Level Programming Abstractions for Distributed Graph Processing2018Ingår i: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, nr 2, s. 305-324Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

  • 44.
    Khan, Amin M.
    et al.
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Ha, Phuong Hoai
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 45.
    Khan, Amin M.
    et al.
    UiT Arctic Univ Norway, Dept Comp Sci, Tromso, Norway.;Hitachi Vantara Corp, Lisbon, Portugal..
    Freitag, Felix
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Ha, Phuong Hoai
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018Ingår i: IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), IEEE , 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi. net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 46. Koubarakis, M.
    et al.
    Bereta, K.
    Bilidas, D.
    Giannousis, K.
    Ioannidis, T.
    Pantazi, D. -A
    Stamoulis, G.
    Haridi, Seif
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Bruzzone, L.
    Paris, C.
    Eltoft, T.
    Krämer, T.
    Charalabidis, A.
    Karkaletsis, V.
    Konstantopoulos, S.
    Dowling, J.
    Kakantousis, T.
    Datcu, M.
    Dumitru, C. O.
    Appel, F.
    Bach, H.
    Migdall, S.
    Hughes, N.
    Arthurs, D.
    Fleming, A.
    From copernicus big data to extreme earth analytics2019Ingår i: Advances in Database Technology - EDBT, OpenProceedings, 2019, s. 690-693Konferensbidrag (Refereegranskat)
    Abstract [en]

    Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

  • 47. Lin, X.
    et al.
    Buyya, R.
    Yang, L.
    Tari, Z.
    Choo, K. -KR.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Yao, L.
    Yin, H.
    Wang, W.
    Message from the BDCloud 2018 Chairs2019Ingår i: 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, s. XXIX-XXX, artikel-id 8672358Artikel i tidskrift (Refereegranskat)
  • 48.
    Lindbäck, Leif
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Mokarizadeh, Shahab
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    Violino, Gabriele
    Net Result AB.
    Churn Tolerant Virtual Organization File System for Grids2010Ingår i: PARALLEL PROCESSING AND APPLIED MATHEMATICS, PART II / [ed] Wyrzykowski, R; Dongarra, J; Karczewski, K; Wasniewski, J, Springer Berlin/Heidelberg, 2010, Vol. 6068, s. 194-203Konferensbidrag (Refereegranskat)
    Abstract [en]

    A Grid computing environment allows forming Virtual Organizations (VOs) to aggregate and share resources We present. a VO File System (VOFS) which is a VO-aware distributed file system that allows VO members to share files VOFS supports access and location transparency by maintaining a common file namespace, which is decentralized in order to improve robustness VOFS includes a P2P system of file servers, a VO membership service and a policy and role based security mechanism VOFS can he mounted to a local file system in order to access files using POSIX file API VOFS can operate in a dynamic Grid environment (e g desktop Grids) since it tolerates unplanned resource arrival and departure (churn) while maintaining a single uniform namespace It supports transparent disconnected operations that allow the user to work on files while being disconnected.

  • 49.
    Liu, Ying
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Guan, Xi
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    Haridi, Seif
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    MeteorShower: Minimizing Request Latency for Majority Quorum-Based Data Consistency Algorithms in Multiple Data Centers2017Ingår i: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 57-67, artikel-id 7979955Konferensbidrag (Refereegranskat)
    Abstract [en]

    With the increasing popularity of serving and storing data in multiple data centers, we investigate the efficiency of majority quorum-based data consistency algorithms under this scenario. Because of the failure-prone nature of distributed storage systems, majority quorum-based data consistency algorithms become one of the most widely adopted approaches. In this paper, we propose the MeteorShower framework, which provides fault-tolerant read/write key-value storage service across multiple data centers with sequential consistency guarantees. A major feature is that most read operations are executed locally within a single data center. This results in lowering read latency from hundreds of milliseconds to tens of milliseconds. The data consistency algorithm in MeteorShower augments majority quorum-based algorithms. Thus, it keeps all the desirable properties of majority quorums, such as fault tolerance, balanced load, etc. An implementation of MeteorShower on top of Cassandra is deployed and evaluated in multiple data centers using the Google Cloud Platform. Evaluations of MeteorShower framework have shown that it can consistently serve read requests without paying the communication delays among replicas maintained in multiple data centers. As a result, we are able to improve the latency of read requests from hundreds of milliseconds to tens of milliseconds while achieving the same latency on write requests and the same fault tolerance guarantee. Thus, MeteorShower is optimized for read intensive workloads.

  • 50.
    Liu, Ying
    et al.
    KTH.
    Gureya, Daharewa
    KTH.
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
    OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services2017Ingår i: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 20, nr 3, s. 1977-1994Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/de-allocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

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