计算机科学与探索 ›› 2015, Vol. 9 ›› Issue (10): 1172-1179.DOI: 10.3778/j.issn.1673-9418.1411011

• 高性能计算 • 上一篇    下一篇

基于混合预测的云平台资源自适应分配方法

冯光升1+,赵晓宇1,马军2,高瑞1,吕宏武1,王慧强1,刘永丽1   

  1. 1. 哈尔滨工程大学 计算机科学与技术学院,哈尔滨 150001
    2. 哈尔滨龙江特种装备有限公司,哈尔滨 150001
  • 出版日期:2015-10-01 发布日期:2015-09-29

Adaptive Resource Allocation Approach for Cloud Computing Platform Based on Mixed Prediction

FENG Guangsheng1+, ZHAO Xiaoyu1, MA Jun2, GAO Rui1, LV Hongwu1,WANG Huiqiang1, LIU Yongli1   

  1. 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    2. Harbin Longjiang Special Equipment Co., Ltd., Harbin 150001, China
  • Online:2015-10-01 Published:2015-09-29

摘要: 针对云平台下资源利用率低,缺乏资源变化的准确预测等问题,提出了基于混合预测的云平台资源分配方法。该方法根据服务资源需求的周期性特点,采用快速傅里叶变换(fast Fourier transform,FFT)的手段判断服务资源需求的周期性,对缺乏周期性的资源请求采用马尔科夫过程进行预测,进而可依据预测结果自适应地分配虚拟机资源。实验结果表明,该方法能够准确预测服务资源需求,合理分配虚拟机资源,提高了虚拟机资源利用率,有效降低了SLA(service-level agreement)的违反次数。

关键词: 虚拟机, 资源分配, 快速傅里叶变换, 马尔科夫链

Abstract: For the lower efficiency of resource allocation and lacking precise prediction for resource requirement, this paper proposes a resource allocation approach based on mixed prediction. According to the cyclical feature of resource requirement, this proposed approach employs FFT (fast Fourier transform) tools to determine the cyclical attribution. If there is no such attribution existed, the Markov chain is alternatively used to predict the tendency of resource requirement. The experimental results show that the proposed approach can predict the future resource requirement more precisely. Moreover, the proposed approach can also allocate the virtual machine resource adaptively, decrease the number of occupied physical machines, and reduce the number of violating the SLA (service-level agreement).

Key words: virtual machine, resource allocation, fast Fourier transform, Markov chain