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Scientific Programming
Volume 2016, Article ID 6208358, 13 pages
Research Article

Task Classification Based Energy-Aware Consolidation in Clouds

1Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
2IT Convergence Education Center, Dongguk University, Seoul, Republic of Korea
3Department of Computer Science, Dongduk Women’s University, Seoul, Republic of Korea

Received 22 January 2016; Accepted 3 August 2016

Academic Editor: Zhihui Du

Copyright © 2016 HeeSeok Choi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.