NDLI: Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing

Content Provider	IEEE Xplore Digital Library
Author	Farahnakian, F. Pahikkala, T. Liljeberg, P. Plosila, J. Tenhunen, H.
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Technol., Univ. of Turku, Turku, Finland (Farahnakian, F.; Pahikkala, T.; Liljeberg, P.; Plosila, J.; Tenhunen, H.)
Abstract	Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption and improve resource utilization in data centers. Since VM consolidation problem is strictly NP-hard, many heuristic algorithms have been proposed to tackle the problem. However, most of the existing works deal only with minimizing the number of hosts based on their current resource utilization and these works do not explore the future resource requirements. Therefore, unnecessary VM migrations are generated and the rate of Service Level Agreement (SLA) violations are increased in data centers. To address this problem, our VM consolidation method which is formulated as a bin-packing problem considers both the current and future utilization of resources. The future utilization of resources is accurately predicted using a k-nearest neighbor regression based model. In this paper, we investigate the effectiveness of VM and host resource utilization predictions in the VM consolidation task using real workload traces. The experimental results show that our approach provides substantial improvement over other heuristic algorithms in reducing energy consumption, number of VM migrations and number of SLA violations.
Starting Page	381
Ending Page	388
File Size	456743
Page Count	8
File Format	PDF
ISSN	21596190
e-ISBN	9781467372879
DOI	10.1109/CLOUD.2015.58
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Heuristic algorithms Energy-efficiency Switches Predictive models Prediction algorithms SLA Utilization prediction model Dynamic VM consolidation Resource management Bin-paking Capacity planning
Content Type	Text
Resource Type	Article

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