SEARCH WITHIN CONTENT
Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 2, Pages 120-129, DOI: https://doi.org/10.21307/ijanmc-2017-014
License : (CC BY-NC-ND 4.0)
Published Online: 09-April-2018
How to improve the resource utilization of the cloud computing system has been one of the key content of the research of the cloud computing. The traditional multi-objective ant colony optimization was improved, studied the virtual machine live migration framework, combined with the elimination method to solve virtual machine migration and placement of multi-objective optimization problem, the load balanced specific strategies are integrated into the framework of a dynamic migration, simulation experiments are carried out and the conclusions are made for it. The algorithm can obtain the optimal solution through the continuous updating of pheromone. The main consideration is the Service level contract violation rate(S), Resource loss(W),Power consumption (P). Experimental results show that ,compared with the traditional heuristic method and genetic algorithm, the algorithm is advantageous to the parallel computation, and it’s able to achieve the optimal tradeoff and compromise between multiple conflicting objectives. In the case of service level contract violation rate is low, system resource waste and power consumption are at the least, so it has feasibility.
HYEAR C,MCKEE B, GARDNER R, et al. Autonomic virtual machine placement in the data center [J]. Hewlett Packard Laboratories, Tech. Rep. HPL-2007-189, 2007:2007-189.
Li Jingchao,ChenJingyi,WuJie. Research on virtual machine placement based on improved grouping genetic algorithm [J]. Computer engineering and design,2012,33(5):2053-2056..
Li Yong: Analysis and Research on dynamic migration of virtual machine[Dissertation]. National Defense Science and Technology University,2007.
Li Zhiwei,WuQingbo,TanYusong. Research on dynamic migration of virtual machine based on device agent mechanism. Computer application research. Twenty-sixth volumes, April 2009.
CAREY M R，JOHNSON D S. Computers and ln tractability: a guide to NP-completeness [J].1979.
CALHEIROS R N, RANJAN R, ct al. Cloud Sim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J]. Software: Practice and Experience, 2011, 41(1): 23-50.
Sun Hong, Zhang Huaxuan, Chen Shiping, etal. The Study of Improved FP-Growth Algorithm in Map Reduce[C].//Proc.CPCI- 1st International Workshop on cloud Computing and Information Security(CCIS) 2013 CPCI:00033591040005