ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (11): 2516-2533.doi: 10.7544/issn1000-1239.2017.20160700

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Improving Cloud Platform Based on the Runtime Resource Capacity Evaluation

Zhou Mosong, Dong Xiaoshe, Chen Heng, Zhang Xingjun   

  1. (School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049)
  • Online:2017-11-01

Abstract: There is a mismatch between computing resource supply and demand in cloud computing platform resource management, which leads to the performance degradation. This paper establishes a runtime computing resource available capacity evaluation model base on similar tasks. The model uses the characteristic of cloud computing workload in which similar tasks have the same execution logic, evaluates computing resource available capacity according to similar tasks avoiding computing resource consumption in executing benchmark. This paper applies the model to propose a computing resource capacity evaluation method called RCE, which considers many factors and evaluates runtime computing resource available capacity classified by resource type. This method gets accurate evaluation results timely with little cost. We apply RCE results in some algorithms to match computing resource supply and demand, and improve cloud computing platform performance. We test RCE method and algorithms base on RCE in dedicated and real cloud computing environments. The test results show that the RCE method gets runtime evaluation results timely and the evaluation results reflect computing resource available capacity accurately. Moreover, the RCE method supports the optimization of algorithm and platform effectively. And algorithms base on RCE resolve the mismatch problem between resource supply and demand, and significantly improve the performance of cloud computing platform.

Key words: cloud computing, resource capacity evaluation (RCE), similar task, resource management, platform optimization

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