ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Real-time multitask load balance algorithm for heterogeneous cloud computing platforms

  • 1.

    Computer School, Wuhan University, Wuhan 430072, China

  • 2.

    Department of Basic Knowledge, Military Economy Academy, Wuhan 430035, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.03.006
  • Received Date: 27 August 2015
  • Accepted Date: 01 December 2015
  • Rev Recd Date: 01 December 2015
  • Publish Date: 30 March 2016
    Abstract

  • Abstract

    A load-balancing algorithm applying in heterogeneous cloud Computing Platform handling real-time multitasks was proposed. Average hardware resource consumption of jobs running on nodes was measured. Balancing server receive load status of each node in the cluster periodically. A load status vector that reflects the quantity of resources required to finish allocated jobs of each node can be estimated according to the latest load status report and other parameters. As a request is submitted to the cluster, Balancing Server calculates the load status estimation vector of each node, and then dispatches it to the node that possesses the minimal load status estimation value. Experiment results proved that this dynamic load balancing algorithm is reasonable and effective.

    Abstract

    A load-balancing algorithm applying in heterogeneous cloud Computing Platform handling real-time multitasks was proposed. Average hardware resource consumption of jobs running on nodes was measured. Balancing server receive load status of each node in the cluster periodically. A load status vector that reflects the quantity of resources required to finish allocated jobs of each node can be estimated according to the latest load status report and other parameters. As a request is submitted to the cluster, Balancing Server calculates the load status estimation vector of each node, and then dispatches it to the node that possesses the minimal load status estimation value. Experiment results proved that this dynamic load balancing algorithm is reasonable and effective.
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    • References(16)
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    RODRIGUES E R, NAVAUX P O A, PANETTA J, et al. A comparative analysis of load balancing algorithms applied to a weather forecast model[C]// Proceeding of 22nd International Symposium on Computer Architecture and High Performance Computing. Petrópolis, Brazil: IEEE Press, 2010: 71-78.
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    YUN S Y, PROUTIERE A. Distributed proportional fair load balancing in heterogeneous systems[J]. ACM Sigmetrics Performance Evaluation Review, 2015, 43(1): 17-30.
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    JOE-WANG C, SEN S, LAN T, et al. Multi-resource allocation: Fairness-efficiency tradeoffs in a unifying framework[J]. IEEE/ACM Transactions on Networking, 2013, 21(6): 1785-1798.
    [12]
    张玉芳,魏钦磊,赵膺.基于负载权值的负载均衡算法[J].计算机应用研究, 2012, 29(12): 4711-4713.
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    ZAHARIA M. Job scheduling with the fair and capacity schedulers[J]. Hadoop Summit, Santa Clara, USA, 2009: 9-18.
    [14]
    陈廷伟,周山杰,秦明达.面向云计算的任务分类方法[J].计算机应用, 2012, 32(10): 2719-2723, 2727.
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    SHI W J, ZHANG L Q, WU C, et al. An online auction framework for dynamic resource provisioning in cloud computing[J]. ACM SIGMETRICS Performance Evaluation Review, 2014, 42(2): 71-83.
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    BEAUMONT O, MARCHAL L. Analysis of dynamic scheduling strategies for matrix multiplication on heterogeneous platforms[C]// Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing. Vancouver, Canada: ACM Press. 2014: 141-152.)
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    [1]
    张建勋,古志民,郑超.云计算研究进展综述[J].计算机应用研究, 2010,27(2): 429-433.
    [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.
    [3]
    BONALD J, ROBERTS J. Multi-resource fairness: Objectives, algorithms and performance[J]. ACM SIGMETRICS Performance Evaluation Review, 2015, 43(1): 31-42.
    [4]
    胡志刚,张艳平.基于目标约束的分层动态负载均衡算法[J].计算机应用研究, 2011, 28(3): 1105-1107.
    [5]
    BRYHNI H, KLOVNING E, KURE O. A comparison of load-balancing techniques for scalable Web servers[J]. IEEE Network: The Magazine of Global Internetworking, 2000, 14(4): 58-64.
    [6]
    KELLER M, KARI H. Response time-optimized distributed cloud resource allocation[C]// Proceedings of the ACM SIGCOMM workshop on Distributed cloud computing. Chicago, USA: ACM Press, 2014:47-52.
    [7]
    PEARCE O, GAMBLIN T, DE SUPINSKI B, et al. Quantifying the effectiveness of load balance algorithms[C]// Proceedings of the 26th ACM International Conference on Supercomputing. Venice, Italy: ACM Press, 2012: 185-194.
    [8]
    张宇翔, 张宏科. 一种层次结构化 P2P 网络中的负载均衡方法[J].计算机学报, 2010, 33(9): 1580-1590.
    [9]
    RODRIGUES E R, NAVAUX P O A, PANETTA J, et al. A comparative analysis of load balancing algorithms applied to a weather forecast model[C]// Proceeding of 22nd International Symposium on Computer Architecture and High Performance Computing. Petrópolis, Brazil: IEEE Press, 2010: 71-78.
    [10]
    YUN S Y, PROUTIERE A. Distributed proportional fair load balancing in heterogeneous systems[J]. ACM Sigmetrics Performance Evaluation Review, 2015, 43(1): 17-30.
    [11]
    JOE-WANG C, SEN S, LAN T, et al. Multi-resource allocation: Fairness-efficiency tradeoffs in a unifying framework[J]. IEEE/ACM Transactions on Networking, 2013, 21(6): 1785-1798.
    [12]
    张玉芳,魏钦磊,赵膺.基于负载权值的负载均衡算法[J].计算机应用研究, 2012, 29(12): 4711-4713.
    [13]
    ZAHARIA M. Job scheduling with the fair and capacity schedulers[J]. Hadoop Summit, Santa Clara, USA, 2009: 9-18.
    [14]
    陈廷伟,周山杰,秦明达.面向云计算的任务分类方法[J].计算机应用, 2012, 32(10): 2719-2723, 2727.
    [15]
    SHI W J, ZHANG L Q, WU C, et al. An online auction framework for dynamic resource provisioning in cloud computing[J]. ACM SIGMETRICS Performance Evaluation Review, 2014, 42(2): 71-83.
    [16]
    BEAUMONT O, MARCHAL L. Analysis of dynamic scheduling strategies for matrix multiplication on heterogeneous platforms[C]// Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing. Vancouver, Canada: ACM Press. 2014: 141-152.)
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