GASd: a GA-based Strategy Decision Algorithm for Rearranging Virtual Machines

• Main Authors: Li, Zong-Xian, 李宗憲
• Other Authors: 黃育綸
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/y523bw

碩士 === 國立交通大學 === 電機工程學系 === 104 === In cloud computing, users can save hardware cost by using cloud resources provided and managed by cloud service provider (CSP). If an inappropriate distribution strategy is applied and a load unbalancing situation occurs, the virtual machines run on an overloading host can not work well. Thus, keeping load balancing is an issue for a CSP. In this research, we present a resource reallocating framework for a CSP to manage resources and keep physical machines in a load balanced state. The proposed framework uses probes to collect CPU utilization data and send the collection to the control server. Then the control server makes a distribution strategy using the proposed algorithm, GASd. GASd is an algorithm based on Genetic Algorithm(GA). The design of GASd allows a CSP to have preference to a load dimension of CPU or memory, and also to control the migration ratio of virtual machines. We conduct several experiments to evaluate the ability of GASd. In the experiments, we show that GASd can reduce the load deviation among physical machines to about 3% for both load dimensions in about 0.25 seconds. GASd can make a strategy to minimize utilization standard deviation of one load dimension with acceptable standard deviation of another load dimension. We also discuss the scalability of GASd to distribute 10000 virtual machines to 2500 physical machines, and GASd can find an optimal distribution strategy for the running cloud system in 2.5 minutes with distance 6.25%.