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检索条件"主题词=distributed Computing"

共 27587 条记录，以下是281-290 订阅

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Resources Co-Allocation Optimization Algorithms for distributed computing Environments 18

Resources Co-Allocation Optimization Algorithms for Distribu...

引用

47th International Conference on Parallel Processing (ICPP) / International Workshop on Embedded Multicore Systems (EMS)

作者： Toporkov, Victor Yemelyanov, Dmitry Natl Res Univ Moscow Power Engn Inst Moscow Russia

ISBN: (纸本)9781450365239

In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources (clusters, CPU nodes equipped with multicore processors, networks etc.). A single slot is a time span that can be assigned to a task, which is a part of a parallel job. The job launch requires a co-allocation of a specified number of slots starting and finishing synchronously. The challenge is that slots associated with different heterogeneous resources of distributed computing environments may have arbitrary start and finish points, different performance, latency, pricing policies. Some existing algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for efficient slot selection are studied and compared with known approaches. The novelty of the proposed approach is in a general algorithm selecting a set of slots efficiently according to the specified criterion.

关键词： Scheduling distributed computing Resources Co-allocation Optimization Slot Job Window

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Will distributed computing Revolutionize Peace? The Emergence of Battlefield IoT 38

Will Distributed Computing Revolutionize Peace? The Emergenc...

引用

38th IEEE International Conference on distributed computing Systems (ICDCS)

作者： Abdelzaher, Tarek Ayania, Nora Basar, Tamer Diggavi, Suhas Diesner, Jana Ganesan, Deepak Govindan, Ramesh Jha, Susmit Lepoint, Tancrede Marlin, Ben Nahrstedt, Klara Nicol, David Rajkumar, Raj Russell, Stephen Seshia, Sanjit Sha, Fei Shenoy, Prashant Srivastava, Mani Sukhatme, Gaurav Swami, Ananthram Tabuada, Paulo Tabuada, Paulo Towsley, Don Vaidya, Nitin Veeravalli, Venu Univ Illinois Urbana IL 61801 USA Univ Massachusetts Amherst MA 01003 USA Univ Calif Los Angeles Los Angeles CA 90095 USA Univ Southern Calif Los Angeles CA 90089 USA Univ Calif Berkeley Berkeley CA 94720 USA SRI Int New York NY 10165 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781538668719

An upcoming frontier for rapidly reconfigurable distributed analytics and services might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (JOT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.

关键词： Sensors Task analysis Security distributed computing Military computing Urban areas Economics

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Solve Game of the Amazons with Neural Networks and distributed computing 30

Solve Game of the Amazons with Neural Networks and Distribut...

引用

30th Chinese Control and Decision Conference (CCDC)

作者： Chang, Yuanxing Qin, Hongkun Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Shenyang Aerosp Univ Engn Training Ctr Shenyang 110136 Peoples R China

ISBN: (纸本)9781538612446

A training model was constructed with convolution neural network. Its policy network was trained by supervised learning. Then the parameters of the policy network were constantly adjusted by self-play and the game strength was enhanced. So it made it surpassed the existing game programs that simply used - pruning algorithm or Monte Carlo algorithm, By using distributed computing to update the data asynchronously, multiple game groups were allocated to different machines at the same time. In this way, a number of common configuration computers could be made full use of.

关键词： Game of the Amazons Neural Networks distributed computing

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Optimal Assignment of Resources for distributed computing in Real-Time Applications

Optimal Assignment of Resources for Distributed Computing in...

引用

IEEE Colombian Conference on Automatic Control (CCAC)

作者： Juan Noreña Ernesto Pérez Richard Rios Andrés Acosta Jairo Espinosa PAAS-UN Universidad Nacional de Colombia Facultad de Minas Medellín Colombia GAUNAL Universidad Nacional de Colombia Facultad de Minas Medellín Colombia

distributed real-time applications often require an optimal assignment of resources to improve performance. This paper proposes a methodology to optimally assign system resources that enables to minimize the processing and communication time. In this study, we defined a case of study partitioned into six subsystems to be simulated with four available processing units. We used undirected graphs to obtain a representation of the system and subsequently solved the resulting NP-hard problem as a mixed-integer quadratic program (MIQP). We also implemented a comprehensive search as groundwork and compared both methods using computational and global time as metrics. Numerical simulations showed that our methodology obtained both a better assignment of computational resources and significant solution time reduction than the comprehensive search. Moreover, the solution increased the rates of shared information between units during the reconciliation process. This methodology can thus be used in applications like distributed state estimation, distributed control or co-simulation.

关键词： Real-time systems Cost function Mathematical model Central Processing Unit distributed computing Probability distribution

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Coded distributed computing with Node Cooperation Substantially Increases Speedup Factors

Coded Distributed Computing with Node Cooperation Substantia...

引用

IEEE International Symposium on Information Theory (ISIT)

作者： Parrinello, Emanuele Lampiris, Eleftherios Elia, Petros EURECOM Commun Dept Sophia Antipolis France

ISBN: (纸本)9781538647813

This work explores a distributed computing setting where K nodes are assigned fractions (subtasks) of a computational task in order to perform the computation in parallel. In this setting, a well-known main bottleneck has been the internode communication cost required to parallelize the task, because unlike the computational cost which could keep decreasing as K increases, the communication cost remains approximately constant, thus bounding the total speedup gains associated to having more computing nodes. This bottleneck was substantially ameliorated by the recent introduction of coded techniques in the context of MapReduce which allowed each node - at the computational cost of having to preprocess approximately t times more subtasks - to reduce its communication cost by approximately t times. In reality though, the associated speed up gains were severely limited by the requirement that larger t and K necessited that the original task be divided into an extremely large number of subtasks. In this work we show how node cooperation, along with a novel assignment of tasks, can help to dramatically ameliorate this limitation. The result applies to wired as well as wireless distributed computing and it is based on the idea of having groups of nodes compute identical mapping tasks and then employing a here-proposed novel D2D coded caching algorithm. In this context, the new approach here manages to achieve a virtual decomposition of the fully connected D2D setting into parallel ones, which significantly reduces the required subpacketization.

关键词： Task analysis distributed computing Delays Device-to-device communication Wireless communication Encoding"

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Hierarchical Coding for distributed computing

Hierarchical Coding for Distributed Computing

引用

IEEE International Symposium on Information Theory (ISIT)

作者： Park, Hyegyeong Lee, Kangwook Sohn, Jy-yong Suh, Changho Moon, Jaekyun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781538647813

Coding for distributed computing supports low-latency computation by relieving the burden of straggling workers. While most existing works assume a simple master-worker model, we consider a hierarchical computational structure consisting of groups of workers, motivated by the need to reflect the architectures of real-world distributed computing systems. In this work, we propose a hierarchical coding scheme for this model, as well as analyze its decoding cost and expected computation time. Specifically, we first provide upper and lower bounds on the expected computing time of the proposed scheme. We also show that our scheme enables efficient parallel decoding, thus reducing decoding costs by orders of magnitude over non-hierarchical schemes. When considering both decoding cost and computing time, the proposed hierarchical coding is shown to outperform existing schemes in many practical scenarios.

关键词： distributed computing Task analysis Encoding Decoding Computational modeling Switches"

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Real-Time distributed computing at Network Edges for Large Scale Industrial IoT Networks

Real-Time Distributed Computing at Network Edges for Large S...

引用

IEEE World Congress on Services (IEEE SERVICES)

作者： Oyekanlu, Emmanuel Scoles, Kevin Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9781538673744

Low-cost, real-time digital signal processors (DSPs) embedded in generic Internet of Things (IoT) edge devices can make significant contributions to distributed edge computing for industrial IoT (IIoT) networks. The DSP considered in this paper is the Texas Instruments (TI) TMS320C28x DSP (C28x). At the edge of the network, the C28x is programmed using low-level Embedded C programming language to construct the Morlet wavelet. Our implementation at this layer is the first known construction of the Morlet wavelet for C28x DSP using Embedded C. At the fog layer, near the edge of the IoT network, where more computing resources exist, the wavelet is then convolved with healthcare (electrocardiogram) and electrical network signals, using Matlab to reduce signal noise, and to identify important parts of examined signals. Convolution results indicates that the distributed computing approach for low-cost generic devices considered in this paper is suitable for use in large IIoT networks.

关键词： Industrial IoT real-time wavelets software development embedded C distributed computing

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Optimization of Resources Selection for Jobs Scheduling in Heterogeneous distributed computing Environments 18th

Optimization of Resources Selection for Jobs Scheduling in H...

引用

18th International Conference on Computational Science (ICCS)

作者： Toporkov, Victor Yemelyanov, Dmitry Natl Res Univ Moscow Power Engn Inst Ul Krasnokazarmennaya 14 Moscow 111250 Russia

ISBN: (纸本)9783319937014;9783319937007

In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources (clusters, CPU nodes equipped with multicore processors, networks etc.). A single slot is a time span that can be assigned to a task, which is a part of a parallel job. The job launch requires a co-allocation of a specified number of slots starting and finishing synchronously. The challenge is that slots associated with different heterogeneous resources of distributed computing environments may have arbitrary start and finish points, different pricing policies. Some existing algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for effective slot selection are studied and compared with known approaches. The novelty of the proposed approach is in a general algorithm selecting a set of slots efficient according to the specified criterion.

关键词： distributed computing Economic scheduling Resource management Slot Job Allocation Optimization

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Zeno: A Straggler Diagnosis System for distributed computing Using Machine Learning 33rd

Zeno: A Straggler Diagnosis System for Distributed Computing...

引用

33rd International Conference on High Performance computing (ISC High Performance)

作者： Shen, Huanxing Li, Cong Intel Corp Shanghai Peoples R China

ISBN: (纸本)9783319920405;9783319920399

Modern distributed computing frameworks for cloud computing and high performance computing typically accelerate job performance by dividing a large job into small tasks for execution parallelism. Some tasks, however, may run far behind others, which jeopardize the job completion time. In this paper, we present Zeno, a novel system which automatically identifies and diagnoses stragglers for jobs by machine learning methods. First, the system identifies stragglers with an unsupervised clustering method which groups the tasks based on their execution time. It then uses a supervised rule learning algorithm to learn diagnosis rules inferring the stragglers with their resource assignment and usage data. Zeno is evaluated on traces from a Google's Borg system and an Alibaba's Fuxi system. The results demonstrate that our system is able to generate simple and easy-to-read rules with both valuable insights and decent performance in predicting stragglers.

关键词： distributed computing Straggler diagnosis Unsupervised clustering Supervised rule induction

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A Reinforcement Learning Based Resource Management Approach for Time-critical Workloads in distributed computing Environment

A Reinforcement Learning Based Resource Management Approach ...

引用

IEEE International Conference on Big Data (Big Data)

作者： Liu, Zixia Zhang, Hong Rao, Bingbing Wang, Liqiang Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781538650356

Many data analyzing applications highly rely on timely response from execution, and are referred as time critical data analyzing applications. Due to frequent appearing of gigantic amount of data and analytical computations, running them on large scale distributed computing environments is often advantageous. The workload of big data applications is often hybrid, i.e., contains a combination of time-critical and regular non-time-critical applications. Resource management for hybrid workloads in complex distributed computing environment is becoming more critical and needs more studies. However, it is difficult to design rule-based approaches best suited for such complex scenarios because many complicated characteristics need to be taken into account. Therefore, we present an innovative reinforcement learning (RL) based resource management approach for hybrid workloads in distributed computing environment. We utilize neural networks to capture desired resource management model, use reinforcement learning with designed value definition to gradually improve the model and use 6-greedy methodology to extend exploration along the reinforcement process. The extensive experiments show that our obtained resource management solution through reinforcement learning is able to greatly surpass the baseline rule-based models. Specifically, the model is good at reducing both the missing deadline occurrences for time-critical applications and lowering average job delay for all jobs in the hybrid workloads. Our reinforcement learning based approach has been demonstrated to be able to provide an efficient resource manager for desired scenarios.

关键词： reinforcement learning time-critical distributed computing

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