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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

共 24650 条记录，以下是4731-4740 订阅

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Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers

Modeling and Simulation of QoS-Aware Power Budgeting in Clou...

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Jakub Krzywda Vinícius Meyer Miguel G. Xavier Ahmed Ali-Eldin Per-Olov Östberg César A. F. De Rose Erik Elmroth Department of Computing Science Umeå University Sweden Polytechnic School Pontiﬁcal Catholic University of Rio Grande do Sul Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

关键词： Data centers Power demand Servers Computational modeling Quality of service Data models Cloud computing

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Contextual-Bandit Anomaly Detection for IoT Data in distributed Hierarchical Edge computing

Contextual-Bandit Anomaly Detection for IoT Data in Distribu...

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international conference on distributed computing Systems

作者： Mao V. Ngo Tie Luo Hakima Chaouchi Tony Q.S. Quek Singapore University of Technology and Design Singapore Institute for Infocomm Research A*STAR Singapore Missouri University of Science and Technology USA CNRS SAMOVAR Telecom Sud Paris Institut Mines Telecom Paris-Saclay University France

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff 1 .

关键词： Adaptation models Computational modeling distributed databases Delays Task analysis Anomaly detection Context modeling

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

引用

20th international conference on distributed computing and Networking, ICDCN 2019

ISBN: (纸本)9781450360944

The proceedings contain 78 papers. The topics discussed include: the weakest failure detector to solve the mutual exclusion problem in an unknown dynamic environment;multi-tenant mobile offloading systems for real-time computer vision applications;distributed symmetry-breaking with improved vertex-averaged complexity;parallel algorithms for predicate detection;improving efficacy of concurrent internal binary search trees using local recovery;session guarantees with raft and hybrid logical clocks;reconfigurable dataflow graphs for processing-in-memory;on the hardness of the strongly dependent decision problem;and cache attacks on blockchain based information centric networks: an experimental evaluation.

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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international conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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A Preliminary Model for Optimal Load Distribution in Heterogeneous Smart Environments 20

A Preliminary Model for Optimal Load Distribution in Heterog...

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IEEE 20th international conference on High Performance Switching and Routing (HPSR)

作者： Garcia, Jordi Aguilo, Francesc Simo, Ester Zaragoza, Marisa Masip-Bruin, Xavi Lu, Yunsong Prieto-Gonzalez, Andres Molinas-Ramon, Joan UPC BarcelonaTech Adv Network Architectures Lab CRAAX Vilanova I La Geltru 08800 Spain Huawei Technol Santa Clara CA 95050 USA

ISBN: (纸本)9781728116860

Smart cities are becoming popular and, with them, a plethora of resources is emerging turning into a massive concentration of computing devices. In such environments, the deployment of a smart resources management system is envisioned as an extraordinary opportunity to provide almost unlimited computing and networking capabilities obtained by putting together a vast set of nearby devices, hence offering high performance computing while benefiting from low latency networking. In this scenario, where millions of highly heterogeneous devices are available, a key challenge is to select the set of devices suited the most to carry out some specific tasks. In this paper, we present a performance estimation model intended to find an optimal solution for load distribution over massive heterogeneous systems, considering the effects of both, distributed computing and network overhead. The current implementation is in a preliminary stage, considering only fully parallel applications. Trials conducted considering several hardware configurations, with either homogeneous or heterogeneous systems, show the fact that the predicted performance matches the one obtained by the experimental executions with an almost negligible deviation. These initial results encourage us to continue developing the model for more complex and realistic applications.

关键词： Computational modeling Load modeling Task analysis Urban areas Performance evaluation Cloud computing Optimization

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31st international Workshop on Languages and Compilers for parallel computing, LCPC 2018

31st International Workshop on Languages and Compilers for P...

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31st international Workshop on Languages and Compilers for parallel computing, LCPC 2018

ISBN: (纸本)9783030346263

The proceedings contain 14 papers. The special focus in this conference is on Languages and Compilers for parallel computing. The topics include: GASNet-EX: A high-performance, portable communication library for exascale;nested parallelism with algorithmic skeletons;HDArray: parallel array interface for distributed heterogeneous devices;automating the exchangeability of shared data abstractions;design and performance analysis of real-time dynamic streaming applications;a similarity measure for gpu kernel subgraph matching;new opportunities for compilers in computer security;footmark: A new formulation for working set statistics;towards an achievable performance for the loop nests;extending index-array properties for data dependence analysis;optimized sound and complete data race detection in structured parallel programs;compiler optimizations for parallel programs.

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Coordinated Resources Allocation Heuristics in distributed computing

Coordinated Resources Allocation Heuristics in Distributed C...

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international conference on Information Technologies in Engineering Education (Inforino)

作者： Victor Toporkov Dmitry Yemelyanov Department of Computing Technologies National Research University "MPEI" Moscow Russia

ISBN: (数字)9781728148106

ISBN: (纸本)9781728148113

In this work, we propose and study heuristics for parallel jobs execution and efficient resources co-allocation in heterogeneous computing environments. Existing modern job- flow execution features and realities impose many restrictions for the resources allocation procedures and applicable scheduling criteria. Subject to these restrictions a special dynamic programming scheme is proposed to select resources depending on their meta-features as secondary scheduling criteria. Such secondary optimization criteria are commonly referred as breaking a tie rules. Thus, while maintaining the same primary scheduling criterion outcomes it is sometimes possible to affect global resources utilization efficiency by choosing the appropriate secondary criteria. Basing on a conservative backfilling algorithm we study different breaking a tie heuristic strategies and their affect on the integral job-flow execution characteristics.

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Linear Systems Solvers for distributed-Memory Machines with GPU Accelerators 25th

Linear Systems Solvers for Distributed-Memory Machines with ...

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25th international conference on parallel and distributed computing (Euro-Par)

作者： Kurzak, Jakub Gates, Mark Charara, Ali YarKhan, Asim Yamazaki, Ichitaro Dongarra, Jack Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab POB 2009 Oak Ridge TN 37831 USA Univ Manchester Manchester M13 9PL Lancs England

ISBN: (纸本)9783030294007;9783030293994

This work presents two implementations of linear solvers for distributed-memory machines with GPU accelerators one based on the Cholesky factorization and one based on the LU factorization with partial pivoting. The routines are developed as part of the Software for Linear Algebra Targeting Exascale (SLATE) package, which represents a sharp departure from the traditional conventions established by legacy packages, such as LAPACK and ScaLAPACK. The article lays out the principles of the new approach, discusses the implementation details, and presents the performance results.

关键词： Linear algebra distributed memory Linear systems of equations Cholesky factorization LU factorization GPU acceleration

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Hardware Counters' Space Reduction for Code Region Characterization 25th

Hardware Counters' Space Reduction for Code Region Character...

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25th international conference on parallel and distributed computing (Euro-Par)

作者： Alcaraz, Jordi Sikora, Anna Cesar, Eduardo Univ Autonoma Barcelona Cerdanyola Del Valles Barcelona Spain

ISBN: (纸本)9783030294007;9783030293994

This work proposes that parallel code regions in an OpenMP application can be characterized using a signature composed by the values of a set of hardware performance counters. Our proposal is aimed towards dynamic tuning and, consequently, the metrics must be collected at execution time, which limits the number of metrics that can be measured. Therefore, our main contribution is the definition of a methodology to determine a reduced set of hardware performance counters that can be measured at application's execution time and that still contains enough information to characterize a parallel region. The proposed methodology is based on principal component analysis and linear correlation analysis. Preliminary results show that it can be used to successfully reduce the number of hardware counters needed to characterize a parallel region, and that this set of counters can be measured at run time with high accuracy and low overhead using counter multiplexing.

关键词： Performance analysis Hardware counters parallel/distributed applications

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