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  • 51. Kolbay, B.
    et al.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    Analyzing last mile delivery operations in barcelona’s urban freight transport network2018In: 2nd EAI International Conference on ICT Infrastructures and Services for Smart Cities, IISSC 2017 and 2nd International Conference on Cloud, Networking for IoT systems, CN4IoT 2017, Springer Verlag , 2018, p. 13-22Conference paper (Refereed)
    Abstract [en]

    Barcelona has recently started a new strategy to control and understand Last Mile Delivery, AreaDUM. The strategy is to provide freight delivery vehicle drivers with a mobile app that has to be used every time their vehicle is parked in one of the designated AreaDUM surface parking spaces in the streets of the city. This provides a significant amount of data about the activity of the freight delivery vehicles, their patterns, the occupancy of the spaces, etc. In this paper, we provide a preliminary set of analytics preceded by the procedures employed for the cleansing of the dataset. During the analysis we show that some data blur the results and using a simple strategy to detect when a vehicle parks repeatedly in close-by parking slots, we are able to obtain different, yet more reliable results. In our paper, we show that this behavior is common among users with 80\% prevalence. We conclude that we need to analyse and understand the user behaviors further with the purpose of providing predictive algorithms to find parking lots and smart routing algorithms to minimize traffic.

  • 52. Koubarakis, M.
    et al.
    Bereta, K.
    Bilidas, D.
    Giannousis, K.
    Ioannidis, T.
    Pantazi, D. -A
    Stamoulis, G.
    Haridi, Seif
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, 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 analytics2019In: Advances in Database Technology - EDBT, OpenProceedings, 2019, p. 690-693Conference paper (Refereed)
    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.

  • 53. Lin, X.
    et al.
    Buyya, R.
    Yang, L.
    Tari, Z.
    Choo, K. -KR.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Yao, L.
    Yin, H.
    Wang, W.
    Message from the BDCloud 2018 Chairs2019In: 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, p. XXIX-XXX, article id 8672358Article in journal (Refereed)
  • 54.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Johansson, Ulf
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Löfström, Tuve
    Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik.
    On the calibration of aggregated conformal predictors2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, 2017, p. 154-173Conference paper (Refereed)
    Abstract [en]

    Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.

  • 55.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fang, Tongtong
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Zhou, Tianyu
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 74762-74771Article in journal (Refereed)
    Abstract [en]

    Intuitive and robust multimodal robot control is the key toward human-robot collaboration (HRC) for manufacturing systems. Multimodal robot control methods were introduced in previous studies. The methods allow human operators to control robot intuitively without programming brand-specific code. However, most of the multimodal robot control methods are unreliable because the feature representations are not shared across multiple modalities. To target this problem, a deep learning-based multimodal fusion architecture is proposed in this paper for robust multimodal HRC manufacturing systems. The proposed architecture consists of three modalities: speech command, hand motion, and body motion. Three unimodal models are first trained to extract features, which are further fused for representation sharing. Experiments show that the proposed multimodal fusion model outperforms the three unimodal models. This paper indicates a great potential to apply the proposed multimodal fusion architecture to robust HRC manufacturing systems.

  • 56.
    Liu, Ying
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Li, Xiaxi
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    GlobLease: A Globally Consistent and Elastic Storage System using Leases2014In: 2014 20TH IEEE INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), IEEE , 2014, p. 701-709Conference paper (Refereed)
    Abstract [en]

    Nowadays, more and more IT companies are expanding their businesses and services to a global scale, serving users in several countries. Globally distributed storage systems are employed to reduce data access latency for clients all over the world. We present GlobLease, an elastic, globally-distributed and consistent key-value store. It is organised as multiple distributed hash tables (DHTs) storing replicated data and namespace. Across DHTs, data lookups and accesses are processed with respect to the locality of DHT deployments. We explore the use of leases in GlobLease to maintain data consistency across DHTs. The leases enable GlobLease to provide fast and consistent read access in a global scale with reduced global communications. The write accesses are optimized by migrating the master copy to the locations, where most of the writes take place. The elasticity of GlobLease is provided in a fine-grained manner in order to precisely and efficiently handle spiky and skewed read workloads. In our evaluation, GlobLease has demonstrated its optimized global performance, in comparison with Cassandra, with read and write latency less than 10 ms in most of the cases. Furthermore, our evaluation shows that GlobLease is able to bring down the request latency under an instant 4.5 times workload increase with skewed key distribution (a zipfian distribution with an exponent factor of 4) in less than 20 seconds.

  • 57.
    Lundberg, Johannes
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Safe Kernel Programming with Rust2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Writing bug free computer code is a challenging task in a low-level language like C. While C compilers are getting better and better at detecting possible bugs, they still have a long way to go. For application programming we have higher level languages that abstract away details in memory handling and concurrent programming. However, a lot of an operating system's source code is still written in C and the kernel is exclusively written in C. How can we make writing kernel code safer? What are the performance penalties we have to pay for writing safe code? In this thesis, we will answer these questions using the Rust programming language. A Rust Kernel Programming Interface is designed and implemented, and a network device driver is then ported to Rust. The Rust code is analyzed to determine the safeness and the two implementations are benchmarked for performance and compared to each other. It is shown that a kernel device driver can be written entirely in safe Rust code, but the interface layer require some unsafe code. Measurements show unexpected minor improvements to performance with Rust.

  • 58. Magnusson, M.
    et al.
    Jonsson, L.
    Villani, M.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models2018In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 27, no 2, p. 449-463Article in journal (Refereed)
    Abstract [en]

    Topic models, and more specifically the class of latent Dirichlet allocation (LDA), are widely used for probabilistic modeling of text. Markov chain Monte Carlo (MCMC) sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler. Supplementary materials for this article are available online.

  • 59.
    Morin, B.
    et al.
    SINTEF Digital, Oslo, Norway.
    Høgenes, J.
    Song, H.
    Harrand, Nicolas Yves Maurice
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Engineering software diversity: A model-based approach to systematically diversify communications2018In: Proceedings - 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018, Association for Computing Machinery, Inc , 2018, p. 155-165Conference paper (Refereed)
    Abstract [en]

    Automated diversity is a promising mean of increasing the security of software systems. However, current automated diversity techniques operate at the bottom of the software stack (operating system and compiler), yielding a limited amount of diversity. We present a novel Model-Driven Engineering approach to the diversification of communicating systems, building on abstraction, model transformations and code generation. This approach generates significant amounts of diversity with a low overhead, and addresses a large number of communicating systems, including small communicating devices.

  • 60.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Crowdsensing-driven Route Optimisation Algorithms for Smart Urban Mobility2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Urban mobility is often considered as one of the main facilitators for greener and more sustainable urban development. However, nowadays it requires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of resources in cities. A high priority for cities around the world is to support residents’ mobility within the urban environments while at the same time reducing congestions, accidents, and pollution. However, developing a more efficient and greener (or in one word, smarter) urban mobility is one of the most difficult topics to face in large metropolitan areas. In this thesis, we approach this problem from the perspective of rapidly evolving ICT landscape which allow us to build mobility solutions without the need for large investments or sophisticated sensor technologies.

    In particular, we propose to leverage Mobile Crowdsensing (MCS) paradigm in which citizens use their mobile communication and/or sensing devices to collect, locally process and analyse, as well as voluntary distribute geo-referenced information. The mobility data crowdsensed from volunteer residents (e.g., events, traffic intensity, noise and air pollution, etc.) can provide valuable information about the current mobility conditions in the city, which can, with the adequate data processing algorithms, be used to route and manage people flows in urban environments.

    Therefore, in this thesis we combine two very promising Smart Mobility enablers – MCS and journey/route planning, and thus bring together to some extent distinct research challenges. We separate our research objectives into two parts, i.e., research stages: (1) architectural challenges in designing MCS systems and (2) algorithmic challenges in MCS-driven route planning applications. We aim to demonstrate a logical research progression over time, starting from fundamentals of human-in-the-loop sensing systems such as MCS, to route optimisation algorithms tailored for specific MCS applications. While we mainly focus on algorithms and heuristics to solve NP-hard routing problems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures.

  • 61.
    Mrazovic, Petar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. Dept. of Computer Architecture, UPC Polytechnic University of Catalonia, Barcelona, Spain.
    Matskin, Mihhail
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems2018In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society , 2018, p. 110-115Conference paper (Refereed)
    Abstract [en]

    Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent such service disruptions, we propose to forecast user demand in terms of expected number of bicycle rentals and returns and accordingly to estimate number of bicycles that need to be manually redistributed among the stations by maintenance vehicles. As opposed to traditional solutions to this problem, which rely on short-term demand forecasts, we aim to maximise the time within which the stations remain balanced by forecasting user demand multiple steps ahead of time. We propose a multi-input multi-output deep learning model based on Long Short-Term Memory networks to forecast user demand over long future horizons. Conducted experimental study over real-world dataset confirms the efficiency and accuracy of our approach.

  • 62.
    Natarajan, Saranya
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. KTH Royal Inst Technol, Stockholm, Sweden..
    Timed C: An Extension to the C Programming Language for Real-Time Systems2018In: 24TH IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2018) / [ed] Pellizzoni, R, IEEE, 2018, p. 227-239Conference paper (Refereed)
    Abstract [en]

    The design and implementation of real-time systems require that both the logical and the temporal behavior are correct. There exist several specialized languages and tools that use the notion of logical time, as well as industrial strength languages such as Ada and RTJS that incorporate direct handling of real time. Although these languages and tools have shown to be good alternatives for safety-critical systems, most commodity real-time and embedded systems are today implemented in the standard C programming language. Such systems are typically targeting proprietary bare-metal platforms, standard POSIX compliant platforms, or open-source operating systems. It is, however, error prone to develop large, reliable, and portable systems based on these APIs. In this paper, we present an extension to the C programming language, called Timed C, with a minimal set of language primitives, and show how a retargetable source-to-source compiler can be used to compile and execute simple, expressive, and portable programs. To evaluate our approach, we conduct a case study of a CubeSat satellite. We implement the core timing aspects in Timed C, and show portability by compiling on-board software to both flight hardware, and to low-cost experimental platforms.

  • 63.
    Niazi, Salman
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Scaling Distributed Hierarchical File Systems Using NewSQL Databases2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For many years, researchers have investigated the use of database technology to manage file system metadata, with the goal of providing extensible typed metadata and support for fast, rich metadata search. However, earlier attempts failed mainly due to the reduced performance introduced by adding database operations to the file system’s critical path. Recent improvements in the performance of distributed in-memory online transaction processing databases (NewSQL databases) led us to re-investigate the possibility of using a database to manage file system metadata, but this time for a distributed, hierarchical file system, the Hadoop Distributed File System (HDFS). The single-host metadata service of HDFS is a well-known bottleneck for both the size of the HDFS clusters and their throughput.In this thesis, we detail the algorithms, techniques, and optimizations used to develop HopsFS, an open-source, next-generation distribution of the HDFS that replaces the main scalability bottleneck in HDFS, single node in-memory metadata service, with a no-shared state distributed system built on a NewSQL database. In particular, we discuss how we exploit recent high-performance features from NewSQL databases, such as application-defined partitioning, partition pruned index scans, and distribution aware transactions, as well as more traditional techniques such as batching and write-ahead caches, to enable a revolution in distributed hierarchical file system performance.HDFS’ design is optimized for the storage of large files, that is, files ranging from megabytes to terabytes in size. However, in many production deployments of the HDFS, it has been observed that almost 20% of the files in the system are less than 4 KB in size and as much as 42% of all the file system operations are performed on files less than 16 KB in size. HopsFS introduces a tiered storage solution to store files of different sizes more efficiently. The tiers range from the highest tier where an in-memory NewSQL database stores very small files (<1 KB), to the next tier where small files (<64 KB) are stored in solid-state-drives (SSDs), also using a NewSQL database, to the largest tier, the existing Hadoop block storage layer for very large files. Our approach is based on extending HopsFS with an inode stuffing technique, where we embed the contents of small files with the metadata and use database transactions and database replication guarantees to ensure the availability, integrity, and consistency of the small files. HopsFS enables significantly larger cluster sizes, more than an order of magnitude higher throughput, and significantly lower client latencies for large clusters.Lastly, coordination is an integral part of the distributed file system operations protocols. We present a novel leader election protocol for partially synchronous systems that uses NewSQL databases as shared memory. Our work enables HopsFS, that uses a NewSQL database to save the operational overhead of managing an additional third-party service for leader election and deliver performance comparable to a leader election implementation using a state-of-the-art distributed coordination service, ZooKeeper.

  • 64.
    Niazi, Salman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Ronström, Mikael
    Haridi, Seif
    KTH.
    Dowling, Jim
    KTH.
    Size Matters: Improving the Performance of Small Files in Hadoop2018Conference paper (Refereed)
    Abstract [en]

    The Hadoop Distributed File System (HDFS) is designed to handle massive amounts of data, preferably stored in very large files. The poor performance of HDFS in managing small files has long been a bane of the Hadoop community. In many production deployments of HDFS, almost 25% of the files are less than 16 KB in size and as much as 42% of all the file system operations are performed on these small files. We have designed an adaptive tiered storage using in-memory and on-disk tables stored in a high-performance distributed database to efficiently store and improve the performance of the small files in HDFS. Our solution is completely transparent, and it does not require any changes in the HDFS clients or the applications using the Hadoop platform. In experiments, we observed up to 61~times higher throughput in writing files, and for real-world workloads from Spotify our solution reduces the latency of reading and writing small files by a factor of 3.15 and 7.39 respectively.

  • 65.
    Olsson, Jakob
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Measuring the Technical and Process Benefits of Test Automation based on Machine Learning in an Embedded Device2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Learning-based testing is a testing paradigm that combines model-based testing with machine learning algorithms to automate the modeling of the SUT, test case generation, test case execution and verdict construction. A tool that implements LBT been developed at the CSC school at KTH called LBTest.

    LBTest utilizes machine learning algorithms with off-the-shelf equivalence- and model-checkers, and the modeling of user requirements by propositional linear temporal logic.

    In this study, it is be investigated whether LBT may be suitable for testing a micro bus architecture within an embedded telecommunication device. Furthermore ideas to further automate the testing process by designing a data model to automate user requirement generation are explored.

  • 66.
    Oz, Isil
    et al.
    Izmir Inst Technol, Comp Engn Dept, TR-35430 Gulbahce, Urla Izmir, Turkey..
    Bhatti, Muhammad Khurram
    Informat Technol Univ, Lahore 54000, Punjab, Pakistan..
    Popov, Konstantin
    SICS Swedish ICT AB, SE-16429 Stockholm, Sweden..
    Brorsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Regression-Based Prediction for Task-Based Program Performance2019In: Journal of Circuits, Systems and Computers, ISSN 0218-1266, Vol. 28, no 4, article id 1950060Article in journal (Refereed)
    Abstract [en]

    As multicore systems evolve by increasing the number of parallel execution units, parallel programming models have been released to exploit parallelism in the applications. Task-based programming model uses task abstractions to specify parallel tasks and schedules tasks onto processors at runtime. In order to increase the efficiency and get the highest performance, it is required to identify which runtime configuration is needed and how processor cores must be shared among tasks. Exploring design space for all possible scheduling and runtime options, especially for large input data, becomes infeasible and requires statistical modeling. Regression-based modeling determines the effects of multiple factors on a response variable, and makes predictions based on statistical analysis. In this work, we propose a regression-based modeling approach to predict the task-based program performance for different scheduling parameters with variable data size. We execute a set of task-based programs by varying the runtime parameters, and conduct a systematic measurement for influencing factors on execution time. Our approach uses executions with different configurations for a set of input data, and derives different regression models to predict execution time for larger input data. Our results show that regression models provide accurate predictions for validation inputs with mean error rate as low as 6.3%, and 14% on average among four task-based programs.

  • 67.
    Palmkvist, Viktor
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Creating domain-specific languages by composing syntactical constructs2019In: 21st International Symposium on Practical Aspects of Declarative Languages, PADL 2019, Springer, 2019, Vol. 11372, p. 187-203Conference paper (Refereed)
    Abstract [en]

    Creating a programming language is a considerable undertaking, even for relatively small domain-specific languages (DSLs). Most approaches to ease this task either limit the flexibility of the DSL or consider entire languages as the unit of composition. This paper presents a new approach using syntactical constructs (also called syncons) for defining DSLs in much smaller units of composition while retaining flexibility. A syntactical construct defines a single language feature, such as an if statement or an anonymous function. Each syntactical construct is fully self-contained: it specifies its own concrete syntax, binding semantics, and runtime semantics, independently of the rest of the language. The runtime semantics are specified as a translation to a user defined target language, while the binding semantics allow name resolution before expansion. Additionally, we present a novel approach for dealing with syntactical ambiguity that arises when combining languages, even if the languages are individually unambiguous. The work is implemented and evaluated in a case study, where small subsets of OCaml and Lua have been defined and composed using syntactical constructs.

  • 68.
    Peiro Sajjad, Hooman
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Methods and Algorithms for Data-Intensive Computing: Streams, Graphs, and Geo-Distribution2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Struggling with the volume and velocity of Big Data has attracted lots of interest towards stream processing paradigm, a paradigm in the area of data-intensive computing that provides methods and solutions to process data in motion. Today's Big Data includes geo-distributed data sources.In addition, a major part of today's Big Data requires exploring complex and evolving relationships among data, which complicates any reasoning on the data. This thesis aims at challenges raised by geo-distributed streaming data, and the data with complex and evolving relationships.

    Many organizations provide global scale applications and services that are hosted on servers and data centers that are located in different parts of the world. Therefore, the data that needs to be processed are generated in different geographical locations. This thesis advocates for distributed stream processing in geo-distributed settings to improve the performance including better response time and lower network cost compared to centralized solutions. In this thesis, we conduct an experimental study of Apache Storm, a widely used open-source stream processing system, on a geo-distributed infrastructure made of near-the-edge resources. The resources that host the system's components are connected by heterogeneous network links. Our study exposes a set of issues and bottlenecks of deploying a stream processing system on the geo-distributed infrastructure. Inspired by the results, we propose a novel method for grouping of geo-distributed resources into computing clusters, called micro data centers, in order to mitigate the effect of network heterogeneity for distributed stream processing applications. Next, we focus on the windowed aggregation of geo-distributed data streams, which has been widely used in stream analytics. We propose to reduce the bandwidth cost by coordinating windowed aggregations among near-the-edge data centers. We leverage intra-region links and design a novel low-overhead coordination algorithm that optimizes communication cost for data aggregation. Then, we propose a system, called SpanEdge, that provides an expressive programming model to unify programming stream processing applications on a geo-distributed infrastructure and provides a run-time system to manage (schedule and execute) stream processing applications across data centers. Our results show that SpanEdge can optimally deploy stream processing applications in a geo-distributed infrastructure, which significantly reduces the bandwidth consumption and response latency.

    With respect to data with complex and evolving relationships, this thesis aims at effective and efficient processing of inter-connected data. There exist several domains such as social network analysis, machine learning, and web search in which data streams are modeled as linked entities of nodes and edges, namely a graph. Because of the inter-connection among the entities in graph data, processing of graph data is challenging. The inter-connection among the graph entities makes it difficult to distribute the graph among multiple machines to process the graph at scale. Furthermore, in a streaming setting, the graph structure and the graph elements can continuously change as the graph elements are streamed. Such a dynamic graph requires incremental computing methods that can avoid redundant computations on the whole graph. This thesis proposes incremental computing methods of streaming graph processing that can boost the processing time while still obtaining high quality results. In this thesis, we introduce HoVerCut, an efficient framework for boosting streaming graph partitioning algorithms. HoVerCut is Horizontally and Vertically scalable. Our evaluations show that HoVerCut speeds up the partitioning process significantly without degrading the quality of partitioning. Finally, we study unsupervised representation learning in dynamic graphs. Graph representation learning seeks to learn low dimensional vector representations for the graph elements, i.e. edges and vertices, and the whole graph.We propose novel and computationally efficient incremental algorithms. The computation complexity of our algorithms depends on the extent and rate of changes in a graph and on the graph density. The evaluation results show that our proposed algorithms can achieve competitive results to the state-of-the-art static methods while being computationally efficient.

  • 69.
    Peiro Sajjad, Hooman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Docherty, Andrew
    Data61, CSIRO.
    Tyshetskiy, Yuriy
    Data61, CSIRO.
    Efficient Representation Learning Using RandomWalks for Dynamic GraphsManuscript (preprint) (Other academic)
    Abstract [en]

    An important part of many machine learning workflows on graphs is vertex representation learning, i.e., learning a low-dimensional vector representation for each vertex in the graph. Recently, several powerful techniques for unsupervised representation learning have been demonstrated to give the state-of-the-art performance in downstream tasks such as vertex classification and edge prediction. These techniques rely on random walks performed on the graph in order to capture its structural properties. These structural properties are then encoded in the vector representation space. 

    However, most contemporary representation learning methods only apply to static graphs while real-world graphs are often dynamic and change over time. Static representation learning methods are not able to update the vector representations when the graph changes; therefore, they must re-generate the vector representations on an updated static snapshot of the graph regardless of the extent of the change in the graph. In this work, we propose computationally efficient algorithms for vertex representation learning that extend random walk based methods to dynamic graphs. The computation complexity of our algorithms depends upon the extent and rate of changes (the number of edges changed per update) and on the density of the graph. We empirically evaluate our algorithms on real world datasets for downstream machine learning tasks of multi-class and multi-label vertex classification. The results show that our algorithms can achieve competitive results to the state-of-the-art methods while being computationally efficient.

  • 70.
    Peiro Sajjad, Hooman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Liu, Ying
    Stockholm University.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Optimizing Windowed Aggregation over Geo-Distributed Data Streams2018In: 2018 IEEE International Conference on Edge Computing (EDGE), IEEE Computer Society Digital Library, 2018, p. 33-41Conference paper (Refereed)
    Abstract [en]

    Real-time data analytics is essential since more and more applications require online decision making in a timely manner. However, efficient analysis of geo-distributed data streams is challenging. This is because data needs to be collected from all edge data centers, which aggregate data from local sources, in order to process most of the analytic tasks. Thus, most of the time edge data centers need to transfer data to a central data center over a wide area network, which is expensive. In this paper, we advocate for a coordinated approach of edge data centers in order to handle these analytic tasks efficiently and hence, reducing the communication cost among data centers. We focus on the windowed aggregation of data streams, which has been widely used in stream analytics. In general, aggregation of data streams among edge data centers in the same region reduces the amount of data that needs to be sent over cross-region communication links. Based on state-of-the-art research, we leverage intra-region links and design a low-overhead coordination algorithm that optimizes communication cost for data aggregation. Our algorithm has been evaluated using synthetic and Big Data Benchmark datasets. The evaluation results show that our algorithm reduces the bandwidth cost up to ~6x, as compared to the state-of-the-art solution.

  • 71.
    Rodriguez-Cancio, Marcelino
    et al.
    University of Rennes 1.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    White, Jules
    Vanderbildt University.
    Images of Code: Lossy Compression for Native Instructions2018Conference paper (Refereed)
    Abstract [en]

    Developers can use lossy compression on images and many other artifacts to reduce size and improve network transfer times. Native program instructions, however, are typically not considered candidates for lossy compression since arbitrary losses in instructions may dramatically affect program output. In this paper we show that lossy compression of compiled native instructions is possible in certain circumstances. We demonstrate that the instructions sequence of a program can be lossily translated into a separate but equivalent program with instruction-wise differences, which still produces the same output. We contribute the novel insight that it is possible to exploit such instruction differences to design lossy compression schemes for native code. We support this idea with sound and unsound program transformations that improve performance of compression techniques such as Run-Length (RLE), Huffman and LZ77. We also show that large areas of code can endure tampered instructions with no impact on the output, a result consistent with previous works from various communities.

  • 72.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Graph-based Analytics for Decentralized Online Social Networks2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks.  DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs.

     

    In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation,  can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate  efficient ML models in massively parallel and highly scalable manner.

  • 73.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community DetectionManuscript (preprint) (Other academic)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to "one size fits all" model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions.

    In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping "one size fits all" model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 74.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Rahimian, Fatemeh
    RISE SICS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community Detection2018In: 38th IEEE International Conference on Distributed Computing Systems, 2018Conference paper (Refereed)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to “one size fits all” model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions. In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with a conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping “one size fits all” model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 75.
    Soto Valero, César
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Benelallam, Amine
    Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
    Harrand, Nicolas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Barais, Olivier
    Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    The Emergence of Software Diversity inMaven Central2019In: 16th International Conference on Mining Software Repositories, Montréal, QC, Canada: IEEE conference proceedings, 2019, p. 333-343Conference paper (Refereed)
    Abstract [en]

    Maven artifacts are immutable: an artifact that isuploaded on Maven Central cannot be removed nor modified. Theonly way for developers to upgrade their library is to releasea new version. Consequently, Maven Central accumulates allthe versions of all the libraries that are published there, andapplications that declare a dependency towards a library can pickany version. In this work, we hypothesize that the immutabilityof Maven artifacts and the ability to choose any version naturallysupport the emergence of software diversity within MavenCentral. We analyze 1,487,956 artifacts that represent all theversions of 73,653 libraries. We observe that more than 30% oflibraries have multiple versions that are actively used by latestartifacts. In the case of popular libraries, more than 50% oftheir versions are used. We also observe that more than 17% oflibraries have several versions that are significantly more usedthan the other versions. Our results indicate that the immutabilityof artifacts in Maven Central does support a sustained level ofdiversity among versions of libraries in the repository.

  • 76.
    Soto-Valero, César
    et al.
    Universidad Central de Las Villas.
    Bourcier, Johann
    University of Rennes.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Detection and Analysis of Behavioral T-patternsi n Debugging Activities2018Conference paper (Refereed)
  • 77.
    Sozinov, Konstantin
    et al.
    KTH.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Human Activity Recognition Using Federated Learning2018In: 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. 1103-1111Conference paper (Refereed)
    Abstract [en]

    State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

  • 78. Thomas, Denez
    et al.
    Harrand, Nicolas
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Bossis, Bruno
    Code{strata} sonifying software complexity2018In: TEI 2018 - Proceedings of the 12th International Conference on Tangible, Embedded, and Embodied Interaction, Association for Computing Machinery (ACM), 2018, p. 617-621Conference paper (Refereed)
    Abstract [en]

    Code{strata} is an interdisciplinary collaboration between art studies researchers (Rennes 2) and computer scientists (INRIA, KTH). It is a sound installation: a computer system unit made of concrete that sits on a wooden desk. The purpose of this project is to question the opacity and simplicity of high-level interfaces used in daily gestures. It takes the form of a 3-D sonification of a full software trace that is collected when performing a copy and paste command in a simple text editor. The user may hear, through headphones, a poetic interpretation of what happens in a computer, behind the of graphical interfaces. The sentence "Copy and paste" is played back in as many pieces as there are nested functions called during the execution of the command.

  • 79. Tran, N. H.
    et al.
    Phung, C. V.
    Nguyen, B. Q.
    Bahri, Leila
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    An effective privacy-preserving data coding in peer-to-peer network2018In: International Journal of Computer Networks & Communications, ISSN 0975-2293, E-ISSN 0974-9322, Vol. 10, no 3, p. 55-74Article in journal (Refereed)
    Abstract [en]

    Coding Opportunistically (COPE) is a simple but very effective data coding mechanism in the wireless network. However, COPE leaves risks for attackers easily getting the private information saved in the packets, when they move through the network to their destination nodes. Hence, a lightweight cryptographic approach, namely SCOPE, was proposed to consolidate COPE against the honest-but-curious and malicious attacks. Honest-but-curious attack serves adversaries who accurately obey the protocol but try to learn as much private information as possible for their curiosity. Additionally, this kind of attack is not destructive consequently. However, it may leave the backdoor for the more dangerous attacks carrying catastrophes to the system. Malicious attack tries to learn not only the private information but also modifies the packet on harmful purposes. To cope with this issue, in this work, a lightweight cryptographic approach improves COPE, namely SCOPE, that is defensive to the both attacks. The private information in the COPE packet are encrypted by Elliptic Curve Cryptography (ECC), and an additional information is inserted into SCOPE packets served for the authentication process using the lightweight hash Elliptic Curve Digital Signature Algorithm (ECDSA). We then prove our new protocol is still guaranteed to be a secure method of data coding, and to be light to effectively operate in the peer-to-peer wireless network.

  • 80.
    Vasiloudis, Theodore
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). RISE.
    Cho, Hyunsu
    AmazonWebServices.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Block-distributed Gradient Boosted Trees2019Conference paper (Refereed)
  • 81.
    Vasiloudis, Theodore
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). RISE.
    De Fransisci Morales, Gianmarco
    ISI Foundation.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Quantifying Uncertainty in Online Regression ForestsManuscript (preprint) (Other academic)
    Abstract [en]

    Accurately quantifying uncertainty in predictions is essential for the deployment of machine learning algorithms in critical applications where mistakes are costly. Most approaches to quantifying prediction uncertainty have focused on settings where the data is static, or bounded. In this paper, we investigate methods that quantify the prediction uncertainty in a streaming setting, where the data is potentially unbounded.

    We propose two meta-algorithms that produce prediction intervals for online regression forests of arbitrary tree models; one based on conformal prediction theory, and the other based on quantile regression. We show that the approaches are able to maintain specified error rates, with constant computational cost per example and bounded memory usage. We provide empirical evidence that the methods outperform the state-of-the-art in terms of maintaining error guarantees, while being an order of magnitude faster. We also investigate how the algorithms are able to recover from concept drift.

  • 82.
    Yalew, Sileshi Demesie
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Mobile Device Security with ARM TrustZone2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Mobile devices such as smartphones are becoming the majority of computing devices due to their evolving capabilities. Currently, service providers such as nancial and healthcare institutions oer services to their clients using smartphone applications (apps). Many of these apps run on Android, the most adopted mobile operating system (OS) today. Since smartphones are designed to be carried around all the time, many persons use them to store their private data. However, the popularity of Android and the open nature of its app marketplaces make it a prime target for malware. This situation puts data stored in smartphones in jeopardy, as it can be stealthily stolen or modied by malware that infects the device.

    With the increasing popularity of smartphones and the increasing amount of personal data  stored on these devices, mobile device security has drawn signicant attention from both industry and academia. As a result, several security mechanisms and tools such as anti-malware software have been proposed for mobile OSs to improve the privacy of private data and to mitigate some of the security risks associated with mobile devices. However, these tools and mechanisms run in the device and assume that the mobile OS is trusted, i.e., that it is part of the trusted computing base (TCB). However, current malware often disables anti-malware software when it infects a device. For mobile phones this trend started more than a decade ago with malware such as the Metal Gear Trojan and Cabir.M, and continues to this day, e.g., with HijackRAT. In this work, we use the ARM TrustZone, a security extension for ARM processors that provides a hardware-assisted isolated environment, to implement security services that are protected from malware even if the mobile OS is compromised.

    In this thesis, we investigate two approaches to address some of the security risks associated with Android-based devices. In the rst approach, we present security services to detect intrusions in mobile devices. We design and implement services for posture assessment (which evaluates the level of trust we can have in the device), for dynamic analysis (which performs dynamic (runtime) analysis of apps using traces of Android application programming interface (API) function calls and kernel syscalls to detect apps for malware), and for authenticity detection (which provides assurance of the authenticity and integrity of apps running on mobile devices). In the second approach, we design and implement a backup and recovery system to protect mobile devices from attacks caused by ransomware attacks, system errors, etc. Finally, we develop a software framework to facilitate the development of security services for mobile devices by combining components of the above services. As proof-of-concept, we implemented a prototype for each service and made experimental evaluations using an i.MX53 development board with an ARM processor with TrustZone.

  • 83.
    Zanni-Merk, Cecilia
    et al.
    Natl Inst Appl Sci Rouen Normandie, LITIS Lab, Normandy, France.;Natl Inst Appl Sci Rouen Normandie, MIND Team, Normandy, France..
    Frydman, Claudia
    Aix Marseille Univ, Marseille, France..
    Håkansson, Anne
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. KTH Royal Inst Technol, Stockholm, Sweden..
    Special Issue: Advances in Knowledge-Based and Intelligent Engineering and Information Systems Preface2018In: DATA TECHNOLOGIES AND APPLICATIONS, ISSN 2514-9288, Vol. 52, no 4, p. 462-462Article in journal (Other academic)
  • 84.
    Zeng, Jingna
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. INESC-ID/Instituto Superior Tecnico, Universidade de Lisboa, Portugal.
    Romano, P.
    Barreto, J.
    Rodrigues, L.
    Haridi, S.
    Online tuning of parallelism degree in parallel nesting transactional memory2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 474-483, article id 8425201Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of self-Tuning the parallelism degree in Transactional Memory (TM) systems that support parallel nesting (PN-TM). This problem has been long investigated for TMs not supporting nesting, but, to the best of our knowledge, has never been studied in the context of PN-TMs. Indeed, the problem complexity is inherently exacerbated in PN-TMs, since these require to identify the optimal parallelism degree not only for top-level transactions but also for nested sub-Transactions. The increase of the problem dimensionality raises new challenges (e.g., increase of the search space, and proneness to suffer from local maxima), which are unsatisfactorily addressed by self-Tuning solutions conceived for flat nesting TMs. We tackle these challenges by proposing AUTOPN, an on-line self-Tuning system that combines model-driven learning techniques with localized search heuristics in order to pursue a twofold goal: i) enhance convergence speed by identifying the most promising region of the search space via model-driven techniques, while ii) increasing robustness against modeling errors, via a final local search phase aimed at refining the model's prediction. We further address the problem of tuning the duration of the monitoring windows used to collect feedback on the system's performance, by introducing novel, domain-specific, mechanisms aimed to strike an optimal trade-off between latency and accuracy of the self-Tuning process. We integrated AUTOPN with a state of the art PN-TM (JVSTM) and evaluated it via an extensive experimental study. The results of this study highlight that AUTOPN can achieve gains of up to 45× in terms of increased accuracy and 4× faster convergence speed, when compared with several on-line optimization techniques (gradient descent, simulated annealing and genetic algorithm), some of which were already successfully used in the context of flat nesting TMs.

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