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Network-aware virtual machine migration in an overcommitted cloud

delete2017-11-01
delete37
PRE
AI
张
张伟哲 (Weizhe Zhang) *
S
Shuo Han
何
何慧 (Hui He)
H
Huixiang Chen
DOI:10.1016/j.future.2016.03.009delete
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Abstract

Abstract

En 中文
Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention. However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center. This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs. The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic, algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Cloud computing
Network-aware virtual machine migration
Network communication costs
Migration costs
Genetic algorithm
Artificial bee colony algorithm
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

F
Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
Papers:
6.9K
Citations:
2.3W

Organization

H
harbin institute of technology
Scholars:
8.0W
Papers: 6.6W
Citations: 66
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