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Issue No. 02 - April-June (2017 vol. 2)
ISSN: 2377-3782
pp: 183-196
Atefeh Khosravi , Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
Lachlan L. H. Andrew , Clayton School of Information Technology, Monash University, Melbourne, Clayton, VIC, Australia
Rajkumar Buyya , Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
ABSTRACT
Cloud data centers consume a large amount of energy that leads to a high carbon footprint. Taking into account a carbon tax imposed on the emitted carbon makes energy and carbon cost play a major role in data centers’ operational costs. To address this challenge, we investigate parameters that have the biggest effect on energy and carbon footprint cost to propose more efficient VM placement approaches. We formulate the total energy cost as a function of the energy consumed by servers plus overhead energy, which is computed through power usage effectiveness (PUE) metric as a function of IT load and outside temperature. Furthermore, we consider that data center sites have access to renewable energy sources. This helps to reduce their reliance on “brown” electricity delivered by off-site providers, which is typically drawn from polluting sources. We then propose multiple VM placement approaches to evaluate their performance and identify the parameters with the greatest impact on the total renewable and brown energy consumption, carbon footprint, and cost. The results show that the approach which considers dynamic PUE, renewable energy sources, and changes in the total energy consumption outperforms the others while still meeting cloud users’ service level agreements.
INDEX TERMS
Cloud computing, Energy consumption, Carbon dioxide, Carbon tax, Servers, Carbon, Green products
CITATION

A. Khosravi, L. L. Andrew and R. Buyya, "Dynamic VM Placement Method for Minimizing Energy and Carbon Cost in Geographically Distributed Cloud Data Centers," in IEEE Transactions on Sustainable Computing, vol. 2, no. 2, pp. 183-196, 2017.
doi:10.1109/TSUSC.2017.2709980
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