Toward Virtual Machine Packing Optimization Based on Genetic Algorithm

Authors:
Hidemoto Nakada;Takahiro Hirofuchi;Hirotaka Ogawa;Satoshi Itoh
Affiliations:
National Institute of Advanced Industrial Science and Technology, Tukuba, Ibaraki, Japan 3058568;National Institute of Advanced Industrial Science and Technology, Tukuba, Ibaraki, Japan 3058568;National Institute of Advanced Industrial Science and Technology, Tukuba, Ibaraki, Japan 3058568;National Institute of Advanced Industrial Science and Technology, Tukuba, Ibaraki, Japan 3058568
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

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Abstract

To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose to employ Genetic Algorithm (GA) method, that is one of the meta-heuristics. We implemented a prototype Virtual Machine packing optimization mechanism on Grivon, which is a virtual cluster management system we have been developing. The preliminary evaluation implied the GA method is promising for the problem.