cs 15(2): e3

Research Article

A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing

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  • @ARTICLE{10.4108/eai.19-8-2015.2260146,
        author={Liyun Zuo and Lei Shu and Shoubin Dong and Zhangbing Zhou and Lei Wang},
        title={A Dynamic Self-adaptive Resource-Load Evaluation Method  in Cloud Computing},
        journal={EAI Endorsed Transactions on Cloud Systems},
        volume={1},
        number={2},
        publisher={EAI},
        journal_a={CS},
        year={2015},
        month={9},
        keywords={cloud computing, energy, load evaluation},
        doi={10.4108/eai.19-8-2015.2260146}
    }
    
  • Liyun Zuo
    Lei Shu
    Shoubin Dong
    Zhangbing Zhou
    Lei Wang
    Year: 2015
    A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing
    CS
    EAI
    DOI: 10.4108/eai.19-8-2015.2260146
Liyun Zuo1, Lei Shu1,*, Shoubin Dong2, Zhangbing Zhou3, Lei Wang4
  • 1: Guangdong University of Petrochemical Technology, China
  • 2: South China University of Technology, China
  • 3: TELECOM SudParis, France
  • 4: Dalian University of Technology, China
*Contact email: lei.shu@live.ie

Abstract

Cloud resource and its load have dynamic characteristics. To address this challenge, a dynamic self-adaptive evaluation method (termed SDWM) is proposed in this paper. SDWM uses some dynamic evaluation indicators to evaluate resource state more accurately. And it divides the resource load into three states -- Overload, Normal and Idle by the self-adaptive threshold. Then it migrates overload resources to balance load, and releases idle resources whose idle times exceed a threshold to save energy, which can effectively improve system utilization. Experimental results demonstrate SDWM has better adaptability than other similar methods when resources dynamically join or exit. This shows the positive effect of the dynamic self-adaptive threshold.