Paper Info
Title
Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers
Abstract
In a typical large-scale data center, a set of applications are hosted over virtual machines (VMs) running on a large number of physical machines (PMs). Such a virtualization technique can be used for conserving power consumption by minimizing the number of PMs that should be turned on according to the application requirements to resource. However, the resource demands for VMs is dynamic in nature since the number of user requests the applications should handle is rapidly changing in practice. It is a great challenge to online reconfigure the VMs (i.e., optimize the number and the locations for the VMs) according to the dynamic resource demands. Especially for the emerging applications of large-scale data centers for cloud computing systems, existing approaches either fails to find a best configuration of VMs or cannot produce a result in an acceptable time. In this paper, we propose an online self-reconfiguration approach for reallocating VMs in large-scale data centers. It first accurately predicts the future workloads of the applications with Brown's quadratic exponential smoothing. Based on such a prediction, it adopts a genetic algorithm to efficiently find the optimal reconfiguration policy. The resource utilization of large-scale cloud computing data centers can thus be improved and their energy consumption can be greatly conserved. We conduct extensive experiments and the results verify that our approach can effectively switch off more unnecessary running PMs comparing with current approaches without a performance degradation of the whole system.
Year
DOI
Venue
2010
10.1109/SCC.2010.69
IEEE SCC
Keywords
Field
DocType
self-adjusting systems,optimal reconfiguration policy,performance guarantee,resource utilization,genetic algorithm,power consumption,computer centres,online self-reconfiguration approach,resource demand,virtual machine,cloud computing data center,virtual machines,energy conservation,data center,typical large-scale data center,resource allocation,energy consumption,large number,reallocating vms,large-scale data center,brown quadratic exponential smoothing,data centers,energy-efficient large-scale cloud computing,physical machine,internet,conserving power consumption,online self-reconfiguration,genetic algorithms,large-scale cloud computing data,dynamic resource demand,cloud computing,virtualization,cloud computing system,exponential smoothing,energy efficient,encoding
Virtualization,Virtual machine,Efficient energy use,Computer science,Resource allocation,Data center,Energy consumption,Control reconfiguration,Distributed computing,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-0-7695-4126-6
53
1.97
References 
Authors
13
6
Name
Order
Citations
PageRank
Haibo Mi113412.60
Wang Huaimin21025121.31
Gang Yin330537.92
Yangfan Zhou41467.98
Dian-Xi Shi510025.92
Lin Yuan6542.38