MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement
Cloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are propos...
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doaj-caadc4bc6c4b4f1d93ffa1f1f75d13c62020-11-25T03:29:31ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/679170679170MTAD: A Multitarget Heuristic Algorithm for Virtual Machine PlacementLei Chen0Jing Zhang1Lijun Cai2Rui Li3Tingqin He4Tao Meng5 College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, ChinaCloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are proposed. Firstly, we propose a physical machine (PM) classification algorithm by analyzing pseudotime complexity and find out an important factor (the number of physical hosts) that affects the efficiency, which improves running efficiency through reduction number of physical hosts; secondly, we present a VM placement optimization model using multitarget heuristic algorithm and figure out the positive and negative vectors of three goals using matrix transformation so as to provide the mapping of VMs to hosts by comparing distance with positive and negative vectors such that the energy consumption is saved, resources wastage of occupied PM is lowered, multidimension resource utilization is optimized, and the running time is shortened. Finally, we consider the poor placement efficiency problem of large-scale virtual serial requests and design a concurrent VM classification algorithm using the K -means method. Simulation experiments validate the performance of the algorithm in four aspects, including placement efficiency, resources utilization balance rate, wastage rate, and energy consumption.https://doi.org/10.1155/2015/679170 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Chen Jing Zhang Lijun Cai Rui Li Tingqin He Tao Meng |
spellingShingle |
Lei Chen Jing Zhang Lijun Cai Rui Li Tingqin He Tao Meng MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement International Journal of Distributed Sensor Networks |
author_facet |
Lei Chen Jing Zhang Lijun Cai Rui Li Tingqin He Tao Meng |
author_sort |
Lei Chen |
title |
MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement |
title_short |
MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement |
title_full |
MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement |
title_fullStr |
MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement |
title_full_unstemmed |
MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement |
title_sort |
mtad: a multitarget heuristic algorithm for virtual machine placement |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2015-10-01 |
description |
Cloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are proposed. Firstly, we propose a physical machine (PM) classification algorithm by analyzing pseudotime complexity and find out an important factor (the number of physical hosts) that affects the efficiency, which improves running efficiency through reduction number of physical hosts; secondly, we present a VM placement optimization model using multitarget heuristic algorithm and figure out the positive and negative vectors of three goals using matrix transformation so as to provide the mapping of VMs to hosts by comparing distance with positive and negative vectors such that the energy consumption is saved, resources wastage of occupied PM is lowered, multidimension resource utilization is optimized, and the running time is shortened. Finally, we consider the poor placement efficiency problem of large-scale virtual serial requests and design a concurrent VM classification algorithm using the K -means method. Simulation experiments validate the performance of the algorithm in four aspects, including placement efficiency, resources utilization balance rate, wastage rate, and energy consumption. |
url |
https://doi.org/10.1155/2015/679170 |
work_keys_str_mv |
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1724578685308108800 |