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An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data
Author(s) -
Dou Wanchun,
Xu Xiaolong,
Meng Shunmei,
Zhang Xuyun,
Hu Chunhua,
Yu Shui,
Yang Jian
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3909
Subject(s) - computer science , provisioning , cloud computing , quality of service , big data , energy consumption , efficient energy use , scheduling (production processes) , distributed computing , virtual machine , server , schedule , computer network , database , operating system , engineering , electrical engineering , operations management
Summary 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. Copyright © 2016 John Wiley & Sons, Ltd.