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distributed Matrix Multiplication Based on Frame Quantization for Straggler Mitigation

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3058-3073页

作者： Son, Kyungrak Choi, Wan Seoul Natl Univ Inst New Media & Commun Seoul 08826 South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea

The number of available worker nodes for distributed computing can be limited and varying in wireless networks with noisy channels. However, the conventional straggler mitigation schemes ensure perfect reconstruction only when the number of worker nodes is more than a predetermined number, called the recovery threshold. When the number of worker nodes is less than the recovery threshold, the conventional schemes show a poor reconstruction performance. To facilitate distributed computing in noisy channels, leveraging the tensor product representation and frame quantization theory, we propose a novel straggler mitigation scheme for distributed matrix multiplication. The proposed scheme offers a reasonable reconstruction performance even when the number of worker nodes is varying and limited. By analyzing the computation error, we figure out the conditions required for encoding and decoding frame design, which suggest using uniform tight frames for encoding and their Moore-Penrose inverse for decoding. The superiority of the proposed scheme to the conventional straggler mitigation schemes when the number of available worker nodes is limited is verified by simulations.

关键词： Encoding Decoding Quantization (signal) Noise measurement Task analysis Signal to noise ratio AWGN channels distributed computing matrix multiplication straggler mitigation frame quantization noisy channel

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A Reliable Platform using Matlab distributed computing Server Integrated with AIM

A Reliable Platform using Matlab Distributed Computing Serve...

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International Conference on High Performance computing and Applications (ICHPCA)

作者： Ding, Xianhua Wang, Xiaoming Du, Yu Li, Jianping Liu, Guoying State Radio Monitoring Ctr Shaanxi Radio Monitoring Stn Xian Shaanxi Peoples R China

ISBN: (纸本)9781479959587

Dell Advanced Infrastructure Manager (AIM) is a software that separates the physical elements in the data center from how to control them. It is convenient to share a pool of backup servers that are ready to take over any other failed server's role. In this paper we integrate Matlab distributed computing Server (MDCS) with AIM to build a reliable, efficient platform for scientific computations. The motivation to use AIM as the resource management center for MDCS is to provide a fault-tolerant computation platform. We test the speedup rate of virtual worker cluster and compare with physical worker cluster using physical client and virtual client. Our experiment show that the performance of virtual worker cluster is about 82% of the physical worker cluster's performance.

关键词： AIM Matlab MDCS distributed computing

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Infusing Cognition into distributed computing A new approach to distributed datacenters with self-managing services on commodity hardware (Virtualized or Not) 23

Infusing Cognition into Distributed Computing A new approach...

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23rd IEEE International Conference on Enabling Technologies, Infrastructure for Collaborative Enterprises (WETICE)

作者： Mikkilineni, Rao Morana, Giovanni C3 DNA Inc Santa Clara CA 95054 USA C3 DNA Inc Catania Italy

ISBN: (纸本)9781479942497

The advent of virtualization technologies and cloud computing has improved application provisioning speed, resource utilization, fault-management, availability using auto-failover and performance optimization using auto-scaling in distributed computing environments. However, heterogeneous virtualization technologies offered by different service providers with disparate infrastructure and their orchestration and management systems have also increased the complexity of managing distributed applications and vendor lock-in. In this paper we utilize new computing, management and programming models introduced using the DIME network architecture to provide end to end service visibility and control across distributed physical or virtual infrastructure. Resulting decoupling of application and service transaction management from myriad distributed infrastructure management systems at run-time enables policy based, secure, service mobility across physical servers or virtual machines deployed in datacenters or public clouds. Using the new architecture we have implemented service self-repair, auto-scaling, live-migration and end-to-end service transaction security independent of server and network security mechanisms.

关键词： business processes service management distributed computing cloud computing cognition DIME network architecture (DNA) Cognitive Container

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Perses: A framework for the continuous evaluation of the QoS of distributed mobile applications

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PERVASIVE AND MOBILE computing 2022年第0期84卷

作者： Laso, Sergio Berrocal, Javier Fernandez, Pablo Ruiz-Cortes, Antonio Murillo, Juan M. Univ Extremadura Caceres Spain Global Proc & Prod Improvement SL Caceres Spain Univ Seville SCORE Lab I3US Inst Seville Spain

The increasing capabilities of mobile devices have led to the emergence of new paradigms exploiting them. These paradigms foster the onload and distribution of functionalities on mobile devices, allowing the development of distributed mobile applications. This distribution reduces the latency and the data traffic overhead and improves privacy. As in any other mobile application, their success largely depends on the quality of service (QoS) they offer. Nevertheless, the evaluation of distributed mobile applications is particularly complex due to the number, heterogeneity, and interactions between the devices involved. Current techniques allow developers to assess the quality of a single device, but they are not designed for highly heterogeneous, distributed, and collaborative environments. This paper presents a framework called Perses, which allows the creation of virtual scenarios with multiple heterogeneous mobile devices to launch end-to-end tests to evaluate not only each device but also the interactions among them. The framework was evaluated against a real deployment, showing that the behavior and the quality attributes measured are similar to those of the real deployment, allowing developers to evaluate these applications before launching them. Finally, Perses was integrated into a DevOps methodology to automate its execution and further facilitate its adoption by software companies. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： distributed computing Mobile applications Quality of Service Evaluation Virtual scenarios

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Editorial: Introduction to the Issue on distributed Machine Learning for Wireless Communication

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 320-325页

作者： Yang, Ping Dobre, Octavia A. Xiao, Ming Di Renzo, Marco Li, Jun Quek, Tony Q. S. Han, Zhu Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Mem Univ Newfoundland St John NF A1B3C5 Canada Royal Inst Technol KTH Sch Elect Engn & Comp Sci Dept Informat Sci & Engn S-10044 Stockholm Sweden Univ Paris Saclay F-91190 Gif Sur Yvette France Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Singapore Univ Technol & Design SUTD Singapore 487372 Singapore Univ Houston Houston TX 77004 USA

The papers in this special section focus on the use of distributed machine learning for wireless communications. With the emergence of new application scenarios (e.g., real-time and interactive services and Internet of Things) and the fast development of smart terminals, wireless data traffic has increased drastically, and the existing wireless networks cannot completely meet the technical requirements of the next generation mobile communication networks, e.g., 6G. In recent years, machine learning-based methods have been considered as potential technologies for 6G, because in wireless communication systems, key issues behind synchronization, channel estimation, signal detection, and iterative decoding can be solved by well-designed machine learning algorithms. Currently, most wireless network machine learning solutions require the training data and learning process to be centralized in one or more data centers. However, these centralized machine learning methods expose disadvantages, e.g., privacy security, significant signaling overhead, increased implementation complexity, and high latency, which limit their practicality. The wireless networks of the future must make quicker and more reliable decisions at the network edge.

关键词： Special issues and sections Machine learning Wireless networks distributed computing Signal processing Reinforcement learning

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distributed application of guideline-based decision support through mobile devices: Implementation and evaluation

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 129卷 102324-102324页

作者： Shalom, Erez Goldstein, Ayelet Ariel, Elior Sheinberger, Moshe Jones, Valerie Van Schooten, Boris Shahar, Yuval Ben Gurion Univ Negev Med Informat Res Ctr Dept Software & Informat Syst Engn Beer Sheva Israel Hadassah Acad Coll Dept Comp Sci Jerusalem Israel Univ Twente Enschede Netherlands Roessingh Res & Dev Enschede Netherlands Ben Gurion Univ Negev Med Informat Res Ctr Dept Software & Informat Syst Engn POB 653 IL-8410501 Beer Sheva Israel

Background: Traditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings. Objectives: To design, implement, and demonstrate the technical and clinical feasibility of a new architecture for a distributed DSS that provides patients with evidence-based guidance, offered through applications running on the patients' mobile devices, monitoring and reacting to changes in the patient's personal environment, and providing the patients with appropriate GL-based alerts and personalized recommendations;and increase the overall robustness of the distributed application of the GL. Methods: We have designed and implemented a novel projection-callback (PCB) model, in which small portions of the evidence-based guideline's procedural knowledge are projected from a projection engine within the central DSS server, to a local DSS that resides on each patient's mobile device. The local DSS applies the knowledge using the mobile device's local resources. The GL projections generated by the projection engine are adapted to the patient's previously defined preferences and, implicitly, to the patient's current context, in a manner that is embodied in the projected therapy plans. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. To support the new model, the initial specification of the GL includes two levels: one for the central DSS, and one for the local DSS. We have implemented a distributed GL-based DSS using the

关键词： distributed computing Clinical guidelines Clinical decision support system Knowledge engineering

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Large-Scale and Scalable Latent Factor Analysis via distributed Alternative Stochastic Gradient Descent for Recommender Systems

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IEEE TRANSACTIONS ON BIG DATA 2022年第2期8卷 420-431页

作者： Shi, Xiaoyu He, Qiang Luo, Xin Bai, Yanan Shang, Mingsheng Chinese Acad Sci Chongqing Engn Res Ctr Big Data Applicat Smart Ci Chongqing 400714 Peoples R China Chinese Acad Sci Chongqing Key Lab Big Data & Intelligent Comp Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Hengrui Chongqing Artificial Intelligence Res Ctr Dept Big Data Analyses Techn Chongqing 401331 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong 999077 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia

Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from recommender systems. However, most LFA-based recommender systems adopt a standard SGD algorithm, which suffers limited scalability when addressing big data. On the other hand, most existing parallel SGD solvers are either under the memory-sharing framework designed for a bare machine or suffering high communicational costs, which also greatly limits their applications in large-scale systems. To address the above issues, this article proposes a distributed alternative stochastic gradient descent (DASGD) solver for an LFA-based recommender. Its training-dependences among latent features are decoupled via alternatively fixing one-half of the features to learn the other half following the principle of SGD but in parallel. It's distribution mechanism consists of efficient data partition, allocation and task parallelization strategies, which greatly reduces its communicational cost for high scalability. Experimental results on three large-scale HiDS matrices generated by real-world applications demonstrate that the proposed DASGD algorithm outperforms state-of-the-art distributed SGD solvers for recommender systems in terms of prediction accuracy as well as scalability. Hence, it is highly useful for training LFA-based recommenders on large scale HiDS matrices with the help of cloud computing facilities.

关键词： Recommender systems Training Optimization Big Data Cloud computing Computational modeling Sparse matrices Recommender system latent factor analysis high-dimensional and sparse matrices alternative stochastic gradient descent distributed computing

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Efficient, Failure Resilient Transactions for Parallel and distributed computing

Efficient, Failure Resilient Transactions for Parallel and D...

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DISCS The International Workshop on Data-Intensive Scalable computing Systems

作者： Lofstead, Jay Dayal, Jai Jimenez, Ivo Maltzahn, Carlos Sandia Natl Labs Livermore CA 94550 USA Georgia Tech Atlanta GA USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781479970384

Scientific simulations are moving away from using centralized persistent storage for intermediate data between workflow steps towards an all online model. This shift is motivated by the relatively slow IO bandwidth growth compared with compute speed increases. The challenges presented by this shift to Integrated Application Workflows are motivated by the loss of persistent storage semantics for node-to-node communication. One step towards addressing this semantics gap is using transactions to logically delineate a data set from 100,000s of processes to 1000s of servers as an atomic unit. Our previously demonstrated Doubly distributed Transactions ((DT)-T-2) protocol showed a high-performance solution, but had not explored how to detect and recover from faults. Instead, the focus was on demonstrating high-performance typical case performance. The research presented here addresses fault detection and recovery based on the enhanced protocol design. The total overhead for a full transaction with multiple operations at 65,536 processes is on average 0.055 seconds. Fault detection and recovery mechanisms demonstrate similar performance to the success case with only the addition of appropriate timeouts for the system. This paper explores the challenges in designing a recoverable protocol for doubly distributed transactions, particularly for parallel computing environments.

关键词： distributed computing Parallel Lines Defect detection on-line model RECOVERY MECHANISM PARALLEL PROCESSING (COMPUTERS) Workflow Servers Shift Semantics semiotics protocols MULTIPLE HEAD OPERATIONS

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High-fidelity Graphics for Dynamically Generated Environments using distributed computing 6

High-fidelity Graphics for Dynamically Generated Environment...

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6th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES)

作者： Bugeja, Keith Debattista, Kurt Spina, Sandro Chalmers, Alan Univ Warwick WMG Coventry W Midlands England

ISBN: (纸本)9781479940561

In many serious games and applications, increasing visual fidelity enhances immersion. While physically-based renderers can produce highly realistic imagery by correctly simulating light propagation, these are impractical in the context of interactive applications due to lengthy computations. For real-time applications, several rasterisation-based techniques have been developed to augment the visual realism in a scene, such as the inclusion of shadows and ambient occlusion. These techniques however, come at the cost of additional compute resources from GPUs. This paper describes a novel technique which takes advantage of the distributed computational resources available at peers participating in a serious game to precompute the light distribution in the virtual environment. This additional information is then included within the vertex geometry of the scene and used at no extra costs whilst rendering. Experimental evaluation shows that the rendered virtual environments are much more realistic and more closely match reference imagery generated by physically-based renderers.

关键词： augmented reality distributed processing graphics processing units light propagation rendering (computer graphics) serious games (computing) GPU ambient occlusion distributed computational resources distributed computing dynamically generated environments high-fidelity graphics interactive applications physically-based renderers rasterisation-based techniques realistic imagery serious games shadow occlusion vertex geometry virtual environment visual fidelity Games Geometry Light sources Lighting Rendering (computer graphics) Virtual environments Visualization Virtual environments LIGHT PROPAGATION distributed computing distributed processing LIGHT SOURCES augmented reality Graphics Processing Unit Visualization Lighting geometry Rendering (computer graphics) Game theory Renderers

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A Multi-Order distributed HOSVD with Its Incremental computing for Big Services in Cyber-Physical-Social Systems

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IEEE TRANSACTIONS ON BIG DATA 2020年第4期6卷 666-678页

作者： Wang, Xiaokang Yang, Laurence T. Chen, Xingyu Wang, Lizhe Ranjan, Rajiv Chen, Xiaodao Deen, M. Jamal Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big services, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation and experimental results from real-life, the high-efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social system data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

关键词： Big data tensor MDHOSVD MIHOSVD distributed computing incremental computing CPSS

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