Cost reduction bounds of proactive management based on request prediction

Ruben Milocco 1 Pascale Minet 2 Eric Renault 3, 4, 5 Selma Boumerdassi 6
1 UNCOMA - Universidad Nacional del Comahue [Neuquén]
2 EVA - Wireless Networking for Evolving & Adaptive Applications
Inria de Paris
3 RS2M - Département Réseaux et Services Multimédia Mobiles
4 IP Paris - Institut Polytechnique de Paris
5 R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
6 CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Data Centers (DCs) need to manage their servers periodically to meet user demand efficiently. Since the cost of the energy employed to serve the user demand is lower when DC settings (e.g. number of active servers) are done a priori (proactively), there is a great interest in studying different proactive strategies based on predictions of requests. The amount of savings in energy cost that can be achieved depends not only on the selected proactive strategy but also on the statistics of the demand and the predictors used. Despite its importance, due to the complexity of the problem it is difficult to find studies that quantify the savings that can be obtained. The main contribution of this paper is to propose a generic methodology to quantify the possible cost reduction using proactive management based on predictions. Thus, using this method together with past data it is possible to quantify the efficiency of different predictors as well as optimize proactive strategies. In this paper, the cost reduction is evaluated using both ARMA (Auto Regressive Moving Average) and LV (Last Value) predictors. We then apply this methodology to the Google dataset collected over a period of 29 days to evaluate the benefit that can be obtained with those two predictors in the considered DC.
Keywords : Data centers management Proactive management Machine learning Pediction Energy cost ARMAX
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Conference papers
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Submitted on : Wednesday, January 8, 2020 - 3:30:22 PM
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Ruben Milocco, Pascale Minet, Eric Renault, Selma Boumerdassi. Cost reduction bounds of proactive management based on request prediction. HPCS 2019: 17th International Conference on High Performance Computing & Simulation, Jul 2019, Dublin, Ireland. pp.1-10. ⟨hal-02432652⟩

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