Real-time power cycling in video on demand data centres using online Bayesian prediction

Sanz Marco, Vicent and Wang, Zheng and Porter, Barry Francis (2017) Real-time power cycling in video on demand data centres using online Bayesian prediction. In: 2017 IEEE 37th International Conference on Distributed Computing Systems. IEEE, pp. 2125-2130. ISBN 9781538617939

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Abstract

Energy usage in data centres continues to be a major and growing concern as an increasing number of everyday services depend on these facilities. Research in this area has examined topics including power smoothing using batteries and deep learning to control cooling systems, in addition to optimisation techniques for the software running inside data centres. We present a novel real-time power-cycling architecture, supported by a media distribution approach and online prediction model, to automatically determine when servers are needed based on demand. We demonstrate with experimental evaluation that this approach can save up to 31% of server energy in a cluster. Our evaluation is conducted on typical rack mount servers in a data centre testbed and uses a recent real-world workload trace from the BBC iPlayer, an extremely popular video on demand service in the UK.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
85530
Deposited By:
Deposited On:
03 Apr 2017 08:48
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 06:33