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An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

journal contribution
posted on 2017-07-25, 00:00 authored by W Dou, X Xu, S Meng, X Zhang, C Hu, Shui Yu, J Yang
Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

History

Journal

Concurrency and computation: practice and experience

Volume

29

Issue

14

Season

Special Issue: Special issue on Big data security and intelligent data in clouds (BDS‐IDC 2016)

Article number

e3909

Pagination

1 - 20

Publisher

John Wiley & Sons

Location

Chichester, Eng.

ISSN

1532-0626

eISSN

1532-0634

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2016, John Wiley & Sons