Title :
A new evolutionary multi-objective algorithm to virtual machine placement in virtualized data center
Author :
Chao Liu ; Chenyang Shen ; Sitian Li ; Sinong Wang
Author_Institution :
Key Lab. of Intell. Perception & Image Understanding, China
Abstract :
Virtual machine (VM) placement is a key technology to improve data center efficiency. Most works consider VM placement problem only with respect to physical machine(PM) or network resource optimization. However, efficient VM placement should be implemented by joint optimization of above two aspects. In this paper, a multi-objective VM placement model to minimize the number of active PMs, minimize communication traffic and balance multi-dimensional resource use simultaneously within the data center is proposed. The improved evolutionary multi-objective algorithm: NS-GGA is also designed to tackle this problem, which incorporates the fast non-dominated sorting of NSGA-II into the Grouping Genetic Algorithms. The simulation results show that, in most cases, our model and algorithm gains significantly in all aspects and yields better solutions compared to the existing methods.
Keywords :
cloud computing; computer centres; genetic algorithms; resource allocation; virtual machines; virtualisation; NS-GGA; NSGA-II; PM; VM placement; communication traffic minimization; data center efficiency; evolutionary multiobjective algorithm; grouping genetic algorithms; multidimensional resource balancing; network resource optimization; nondominated sorting genetic algorithm; physical machine; virtual machine placement; virtualized data center; Algorithm design and analysis; Cloud computing; Genetic algorithms; Optimization; Servers; Vectors; Virtual machining; Cloud computing; Data center; Evolutionary multi-objective algorithm; Grouping genetic algorithm; Non-dominated sorting; Optimal control; Virtual machine placement;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-3278-8
DOI :
10.1109/ICSESS.2014.6933561