Please use this identifier to cite or link to this item: https://doi.org/10.1109/IC2E.2013.21
Title: An auction-based resource allocation model for green cloud computing
Authors: Huu, T.T.
Tham, C.-K. 
Keywords: Cloud computing
Combinatorial auction
Energy-aware
Green cloud computing
Resource allocation
Issue Date: 2013
Citation: Huu, T.T., Tham, C.-K. (2013). An auction-based resource allocation model for green cloud computing. Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2013 : 269-278. ScholarBank@NUS Repository. https://doi.org/10.1109/IC2E.2013.21
Abstract: Cloud computing is emerging as a paradigm for large-scale data-intensive applications. Cloud infrastructures allow users to remotely access to computing power and data over the Internet. Beside the huge economical impact, data centers consume enormous amount of electrical energy, contributing to high operational cost and carbon footprints to the environment. An advanced resource allocation model is therefore needed to not only reduce the energy consumption of data centers but also provide incentives to users to optimize their resource utilization and decrease the amount of energy consumed for executing their application. In particular, we present in this paper a novel resource allocation model using combinatorial auction mechanisms and taking into account the energy parameter. Based on this model, we propose three monotone and truthful algorithms used for winners determination and payments computation, namely exhaustive search algorithm (ESA), linear relaxation based randomized algorithm (LRRA) and green greedy algorithm (GGA). We perform numerical simulations to evaluate the performance of three proposed algorithms. Our numerical simulations show that the green greedy algorithm can significantly reduce the amount of consumed energy while generating higher revenue for cloud providers. © 2013 IEEE.
Source Title: Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/83463
ISBN: 9780769549453
DOI: 10.1109/IC2E.2013.21
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