An Intelligent Optimization Method for Optimal Virtual Machine Allocation in Cloud Data Centers

Document Type

Article

Publication Date

10-1-2020

Abstract

A cloud computing paradigm has quickly developed and been applied widely for more than ten years. In a cloud data center, cloud service providers offer many kinds of cloud services, such as virtual machines (VMs), to users. How to achieve the optimized allocation of VMs for users to satisfy the requirements of both users and providers is an important problem. To make full use of VMs for providers and ensure low makespan of user tasks, we formulate an optimal allocation model of VMs and develop an improved differential evolution (IDE) method to solve this optimization problem, given a batch of user tasks. We compare the proposed method with several existing methods, such as round-robin (RR), min-min, and differential evolution. The experimental results show that it can more efficiently decrease the cost of cloud service providers while achieving lower makespan of user tasks than its three peers. Note to Practitioners-VM allocation is one of the challenging problems in cloud computing systems, especially when user task makespan and cost of cloud service providers have to be considered together. We propose an IDE approach to solve this problem. To show its performance, this article compares the commonly used methods, i.e., RR and min-min, as well as the classic differential evolution method. A cloud simulation platform called CloudSim is used to test these methods. The experimental results show that the proposed one can well outperform its compared ones, and its VM allocation results can achieve the highest satisfaction of both users and providers. The proposed method can be readily applicable to industrial cloud computing systems.

Identifier

85085491260 (Scopus)

Publication Title

IEEE Transactions on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/TASE.2020.2975225

e-ISSN

15583783

ISSN

15455955

First Page

1725

Last Page

1735

Issue

4

Volume

17

Grant

NGII20160207

Fund Ref

National Natural Science Foundation of China

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