NDLI: Optimal Reconfiguration of the Cloud Network for Maximum Energy Savings

Content Provider	IEEE Xplore Digital Library
Author	Kantarci, B. Mouftah, H.T.
Copyright Year	2012
Abstract	With the advent of cloud computing, storage and computing functions are migrating to remote resources such as virtual servers and storage systems which are mostly hosted in the data centers. This migration can ensure significant energy savings as utilization of local resources contribute to 40% of the Greenhouse Gas emissions of the Information and Communication Technologies (ICTs). On the other hand, provisioning of the cloud services needs to be handled carefully since energy consumption of the transport network, as well as the energy consumed by the data centers, is expected to increase. We revisit our previously proposed Mixed Integer Linear Programming (MILP) models that are used to reconfigure the cloud network design with look-ahead demand profile. Due to long runtimes of the MILP models in large-scale scenarios, in this paper, we propose two heuristics to reconfigure the cloud network for provisioning the cloud and Internet computing demands. The first heuristic aims to minimize the propagation delay while the second one targets minimizing the power consumption of the data centers and the transport network. We verify the heuristics through simulations where MILP models are used as the benchmarks. Numerical results show that power minimized provisioning can guarantee significant energy savings in the cloud network with less resource consumption. We also present the energy versus delay trade-off and point out possible solutions.
Starting Page	835
Ending Page	840
File Size	291677
Page Count	6
File Format	PDF
ISBN	9781467313957
DOI	10.1109/CCGrid.2012.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power demand Topology Cloud computing Propagation delay IP networks Energy consumption virtual topology data centers demand provisioning energy-efficiency
Content Type	Text
Resource Type	Article

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