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The Scientific World Journal
Volume 2014 (2014), Article ID 208983, 12 pages
http://dx.doi.org/10.1155/2014/208983
Research Article

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

1Department of Computer Science and Engineering, SGGS World University, Fatehgarh Sahib, Punjab, India
2School of Mathematics and Computer Applications, Thapar University, Patiala, India

Received 30 August 2013; Accepted 29 December 2013; Published 11 March 2014

Academic Editors: F. Di Martino and D.-L. Yang

Copyright © 2014 Supriya Kinger 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.

Abstract

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.