一种云存储环境下的资源调度改进算法
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摘要
如何将用户的海量数据以最小的耗时存储到数据中心,是提高云存储效益、解决其发展瓶颈所需考虑的关键问题。证明了云存储环境下资源调度方案的存储最小耗时问题属于一个NPC问题,再针对现有算法对存储调度因素考虑不全面、调度结果易陷入局部最优等问题,提出了一种全新的资源调度算法。该算法利用三角模糊数层次分析法全面分析调度影响因素,得到存储节点的判断矩阵,用于构造后续的遗传算法目标函数,再将简单遗传算法从解的编码、交叉变异操作及致死染色体自我改善等角度进行创新,使其适用于云存储环境下的大规模资源调度。最后与Open Stack中的Cinder块存储算法及现有改进算法进行了分析比对,实验结果验证了所提算法的有效性,实现了更加高效的资源调度。
How to store the user's massive data into the data center with the minimum time-consuming is the key issue to be considered in improving cloud storage efficiency and solving the bottleneck of its development. This paper first proved that the minimum storage time-consuming of resource scheduling scheme in cloud storage environment belongs to NPC problem. In view of the incomplete consideration of the existing scheduling algorithms and the problem that the scheduling result tends to fall into the local optimum,this paper proposed a new resource scheduling algorithm. The algorithm firstly used the triangular fuzzy analytic hierarchy process method to comprehensively analyze the scheduling effecting factors,and obtained the judgment matrix of storage nodes,which was used to construct the follow-up objective function of genetic algorithm,and then it innovated the simple genetic algorithm from the perspective of encoding,cross-mutation operation and self-improvement of lethal chromosome so that it was suitable for cloud storage environment. Finally,this paper analyzed and compared the Cinder block storage algorithm in OpenStack and the existing improved algorithms. The experimental results verify the effectiveness of the proposed algorithm and achieve more efficient resource scheduling.
How to store the user's massive data into the data center with the minimum time-consuming is the key issue to be considered in improving cloud storage efficiency and solving the bottleneck of its development. This paper first proved that the minimum storage time-consuming of resource scheduling scheme in cloud storage environment belongs to NPC problem. In view of the incomplete consideration of the existing scheduling algorithms and the problem that the scheduling result tends to fall into the local optimum,this paper proposed a new resource scheduling algorithm. The algorithm firstly used the triangular fuzzy analytic hierarchy process method to comprehensively analyze the scheduling effecting factors,and obtained the judgment matrix of storage nodes,which was used to construct the follow-up objective function of genetic algorithm,and then it innovated the simple genetic algorithm from the perspective of encoding,cross-mutation operation and self-improvement of lethal chromosome so that it was suitable for cloud storage environment. Finally,this paper analyzed and compared the Cinder block storage algorithm in OpenStack and the existing improved algorithms. The experimental results verify the effectiveness of the proposed algorithm and achieve more efficient resource scheduling.
引文
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[4] Decandia G,Hastorun D,Jampani M,et al. Dynamo:Amazon’s highly available key-value store[J]. ACM SIGOPS Operating Systems Review,2007,41(6):205-220.
[5] Jackson K,Bunch C,Sigler E. OpenStack cloud computing cookbook[M].[S. l.]:Packt Publishing,2015.
[6]王勇,田博,王端.云存储的多维离线调度算法[J].计算机应用与软件,2017,34(6):309-313.(Wang Yong,Tian Bo,Wang Duan.Multidimensional off-line scheduling algorithm for cloud storage[J].Computer Applications and Software,2017,34(6):309-313.)
[7]肖博.云存储调度关键技术研究与实现[D].成都:电子科技大学,2016.(Xiao Bo. Research and implementation of key technologies of cloud storage scheduling[D]. Chengdu:University of Electronic Science and Technology,2016.)
[8]杜永贵,陈鑫.矩阵编码的遗传算法[J].太原理工大学学报,2012,43(2):111-113,118.(Du Yonggui,Chen Xin. Matrix-coded genetic algorithm[J]. Journal of Taiyuan University of Technology,2012,43(2):111-113,118.)
[9]赵晓凡.在线装箱问题相关近似算法研究[D].北京:北京交通大学,2016.(Zhao Xiaofan. Research on approximate algorithms for online packing problem[D]. Beijing:Beijing Jiaotong University,2016.
[10]苏为华.多指标综合评价理论与方法问题研究[D].厦门:厦门大学,2000.(Su Weihua. Study on the theory and method of multi-index comprehensive evaluation[D]. Xiamen:Xiamen University,2000.)
[11]葛继科,邱玉辉,吴春明,等.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916.(Ge Jike,Qiu Yuhui,Wu Chunming,et al. Review of genetic algorithm[J]. Application Research of Computers,2008,25(10):2911-2916.)