期刊文献+

虚拟机资源概率配置的云计算SEFFD算法 被引量:1

A Cloud Computing SEFFD Algorithm for Probability Distribution of Virtual Machine Resource
下载PDF
导出
摘要 为增强虚拟机资源分配过程性能,有效解决云计算环境下虚拟资源分配的NP-hard问题,利用模拟进化算法结合首次下降算法构建虚拟资源分配优化过程(SEFFD)。首先,构建全新的虚拟资源分配的评估方式,并结合模拟进化过程较强的算法寻优爬坡效果,采用迭代方式实现虚拟资源分配过程的个体选择、评估以及排序进化;其次,以模拟进化(SE)过程所获得资源分配结果为基础,结合首次下降(FFD)算法准则,实现物理主机及虚拟机资源的二次分配,从而获得资源分配效果和效率的同步提升;最后,利用Clound Sim及Gridbus云计算仿真平台对算法性能进行对比测试,实验结果表明所提策略的内存利用率高于60%,处理器利用率大于55%,可有效减少所需物理主机数量,从而降低能耗。 Aiming at the problem of NP hard optimization in the process of cloud computing virtual machine resource allocation, a new method based on cloud computing simulated evolution-first fit decreasing algorithm is proposed to improve the efficiency of virtual resource allocation optimization. Firstly, the optimal degree evaluation scheme of virtual machine resource allocation is put forward by using of the strong ability of climbing of simulated evolution, and for which the choice of virtual resource allocation, e- valuation and sorting process is carried out; Secondly, the first fit decreasing rule was adopted to the sort of virtual machine and physical host resource allocation to improve the efficiency and effectiveness of resource allocation; At last, by comparing the ex- perimental results with the CloundSim Grid Laboratory and Gridbus cloud simulation platform, it shows that the proposed algo- rithm is more than 55% of CPU usage, memory usage rate can reach more than 60% , which can improve the utilization rate of the host resources, and achieve the purpose of energy saving.
出处 《计算机与现代化》 2016年第10期15-20,共6页 Computer and Modernization
基金 广东科学技术职业学院重点科研项目(XJZD201202) 广东省高等职业教育教学改革立项项目(201401091) 广东省优秀青年教师培养计划项目(Yq2014187)
关键词 模拟进化 云计算 虚拟机 概率优度 NP难优化 simulation evolution cloud computing virtual machine probability optimization NP hard optimization
  • 相关文献

参考文献16

  • 1邓维,刘方明,金海,李丹.云计算数据中心的新能源应用:研究现状与趋势[J].计算机学报,2013,36(3):582-598. 被引量:132
  • 2李雪竹,陈国龙.云计算虚拟化平台的内存资源全局优化研究[J].计算机工程,2015,41(7):55-59. 被引量:9
  • 3Seehwan Yoo. Real-time scheduling for Xen-ARM virtual machines[ J ]. IEEE Transactions on Mobile Computing, 2014,13(8) :1857-1867.
  • 4Weng Chuliang, Guo Minyi, Yuan Luo, et al. Hybrid CPU management for adapting to the diversity of virtual machines [J ]. IEEE Transactions on Computers, 2013,62 (7) : 1332-1344.
  • 5Raad P, Secci S, Dung Chi Phung, et al. Achieving sub- second downtimes in large-scale virtual machine migrations with LISP[J]. IEEE Transactions on Network and Service Management, 2014,11 ( 2 ) : 133-143.
  • 6Kaewpuang R, Chaisiri S, Niyato D. Cooperative virtual machine management in smart grid environment [ J ]. IEEE Transactions on Services Computing, 2014,7 ( 4 ) : 546- 560.
  • 7Liu Xinhua, Peng Gaoliang, Liu Xiumei, et al. Disassem- bly sequence planning approach for product virtual mainte- nance based on improved max-min ant system[J]. The In- ternational Journal of Advanced Manufacturing Technology, 2012,59 (5) :829-839.
  • 8艾浩军,龚素文,袁远明.基于多目标演化算法的云计算虚拟机分配策略研究[J].计算机科学,2014,41(6):48-53. 被引量:11
  • 9Tang Maolin, Pan Shenchen. A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers [ J ]. Neural Processing Letters, 2015,41 ( 2 ) : 211-221.
  • 10周舟,胡志刚,宋铁,于俊洋.A novel virtual machine deployment algorithm with energy efficiency in cloud computing[J].Journal of Central South University,2015,22(3):974-983. 被引量:12

二级参考文献47

  • 1DMTF.Open virtualization format specification,DSP0243[S].Portland,OR:DMTF,2009.
  • 2Falkenauer E,Delchambre A.A genetic algorithm for Bin Packing and line balancing[C]//Proceedings of the IEEE International Conference on Robotics and Automation.1992:1186-1192.
  • 3Ulker O,et al.A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing[J].Lecture Notes in Computer Science,2008,5199:1140-1149.
  • 4Emmerich M,et al.An EMO Algorithm Using the Hypervolume Measure as Selection Criterion[J].Lecture Notes in Computer Science,2005,3410:62-76.
  • 5Beume N,et al.SMS-EMOA:Multi-objective selection based on dominated hypervolume[J].European Journal of Operational Research,2007,181(3):1653-1669.
  • 6Calheiros R N,Ranjan R,De Rose C A F,et al.CloudSim:A novel framework for modeling and simulation of cloud computing infrastructures and services[R].GRIDS-TR-2009-1.Parkville,VIC:The University of Melbourne Australia,Grid Computing and Distributed Systems Laboratory,2009.
  • 7SEDAGHAT M, HERNANDEZ F, ELMROTH E. Unifying cloud management: Towards overall governance of business level objectives [C]// 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). Newport Beach: IEEE, 2011: 591-597.
  • 8HE Dian, WU Min, HU Chun-hua. Load-balancing and low cost cloud data replica distribution method in Internet of Things environment [J]. Journal of Central South University (Science and Technology), 2012, 43(4): 1355—1361. (in Chinese).
  • 9HOOPER A. Green computing [J]. Communications of the ACM, 2008,51(10): 1-13.
  • 10RANGANATHAN P, Recipe for efficiency: principles of power-aware computing [J]. Communications of the ACM, 2010, 53(4): 60-67.

共引文献157

同被引文献11

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部