期刊文献+

云计算中相似驱动的并行任务划分方法 被引量:3

Similarity-Driven Parallel Task Partitioning Method for Cloud Computing
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摘要 云计算是并行计算、分布式计算和网格计算等高性能计算的进一步发展,它的异构性、按需等特征对高性能计算提出了新的挑战。针对云计算的典型特征,提出了基于并行任务和云环境相似驱动的任务划分方法。首先用图刻画了并行任务和云环境,建立了图的相似关系及其相似度计算方法;其次给出云计算中拟解决的问题,通过图局部相似和全局相似度偏差最小来实现并行任务和体系结构的异构匹配及按需要求;接着利用F度标号方法给出相似驱动的任务划分算法;最后通过实验和其他划分方法进行比较,阐明了该方法的优点。 Cloud computing is further developed by parallel computing, distributed computing and grid computing. Its characters of heterogeneity and on-demand are new challenge for high performance computing. Based on typical features of cloud computing, this paper proposes a similarity-driven parallel task partitioning method. Firstly, it describes parallel tasks and cloud architecture by graph and builds a graph-similarity definition. Secondly, it provides the problem intended to solve in cloud computing, gives the method by local similarity and minimizing global simi- larity deviation, and realizes heterogeneous matching between parallel task and architecture and on-demand require. Thirdly, by F-degree labels it proposes a similarity-driven task partitioning method. Finally, compared with other methods, the experimental results verify the advantages of the proposed method.
出处 《计算机科学与探索》 CSCD 2012年第8期752-759,共8页 Journal of Frontiers of Computer Science and Technology
基金 国家自然科学基金No.61103068 国家高技术研究发展计划(863)No.2009AA012201 NSFC-微软亚洲研究院联合资助项目No.60970155 教育部博士点基金项目No.20090072110035 上海市优秀学科带头人计划项目No.10XD1404400 高效能服务器和存储技术国家重点实验室开放基金项目No.2009HSSA06~~
关键词 并行任务 云计算 相似驱动 任务划分 parallel task cloud computing similarity-driven task partitioning
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参考文献11

  • 1Sims K. IBM introduces ready-to-use cloud computing col- laboration services get clients started with cloud computing [EB/OL]. (2007) [2012-02]. http://www.03.ibm.com/press/us/en/pressrelease/22613.wss.
  • 2陈康,郑纬民.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348. 被引量:1319
  • 3张光卫,何锐,刘禹,李德毅,陈桂生.基于云模型的进化算法[J].计算机学报,2008,31(7):1082-1091. 被引量:129
  • 4Zaharia M, Konwinski A, Joseph A D, et al. Improving Map- Reduce performance in heterogeneous environments[C]// Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation (OSDI '08). Berkeley, CA, USA: USENIX Association, 2008: 29-42.
  • 5Chandra P, Kumar B. Modeling and analysis to estimate the performance of heterogeneous cluster[C]//Proceedings of the 50th Technical Review of Institution of Engineers, 2009: 113-118.
  • 6Morton K, Balazinska M, Grossman D. ParaTimer: a pro- gress indicator for MapReduce DAGs[C]//Proceedings of the 2010 International Conference on Management of Data (SIGMOD ' 10). New York, NY, USA: ACM, 2010:507-518.
  • 7Verbauwhede I, Schaumont E The happy marriage of archi- tecture and application in next-generation reconfigurable system[C]//Proceedings of the 1st Conference on Computing Frontiers (CF '04), Ischia, Italy, 2004. New York, NY, USA: ACM, 2004: 363-376.
  • 8Moulitsas I, Karypis G. Architecture aware partitioning algorithms[C]//Proceedings of the 8th International Confer- ence on Algorithms and Architectures for Parallel Processing (ICA3PP '08), Agia Napa, Cyprus, 2008. Berlin, Heidel- berg: Springer-Verlag, 2008: 42-53.
  • 9Raymond J W, Gardiner E J, Willett P. RASCAL: calcula- tion of graph similarity using maximum edge subgraphs[J]. The Computer Journal, 2002, 45(6): 631-644.
  • 10Kuramochi M, Karypis G. Finding frequent patterns in a large sparse graph[J]. Data Mining and Knowledge Discovery, 2005,11(3): 243-271.

二级参考文献41

  • 1李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34. 被引量:932
  • 2李德毅,刘常昱,杜鹢,韩旭.不确定性人工智能[J].软件学报,2004,15(11):1583-1594. 被引量:416
  • 3刘习春,喻寿益.局部快速微调遗传算法[J].计算机学报,2006,29(1):100-105. 被引量:37
  • 4Sims K. IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing. 2007. http://www-03.ibm.com/press/us/en/pressrelease/22613.wss
  • 5Boss G, Malladi P, Quan D, Legregni L, Hall H. Cloud computing. IBM White Paper, 2007. http://download.boulder.ibm.com/ ibmdl/pub/software/dw/wes/hipods/Cloud_computing_wp_final_8Oct.pdf
  • 6Zhang YX, Zhou YZ. 4VP+: A novel meta OS approach for streaming programs in ubiquitous computing. In: Proc. of IEEE the 21st Int'l Conf. on Advanced Information Networking and Applications (AINA 2007). Los Alamitos: IEEE Computer Society, 2007. 394-403.
  • 7Zhang YX, Zhou YZ. Transparent Computing: A new paradigm for pervasive computing. In: Ma JH, Jin H, Yang LT, Tsai JJP, eds. Proc. of the 3rd Int'l Conf. on Ubiquitous Intelligence and Computing (UIC 2006). Berlin, Heidelberg: Springer-Verlag, 2006. 1-11.
  • 8Barroso LA, Dean J, Holzle U. Web search for a planet: The Google cluster architecture. IEEE Micro, 2003,23(2):22-28.
  • 9Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine. Computer Networks, 1998,30(1-7): 107-117.
  • 10Ghemawat S, Gobioff H, Leung ST. The Google file system. In: Proc. of the 19th ACM Symp. on Operating Systems Principles. New York: ACM Press, 2003.29-43.

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