Abstract
The optimization of the energy consumed by data centers is a major concern. Several techniques have tried in vain to overcome this issue for many years. In this panoply, predictive approaches start to emerge. They consist in predicting in advance the resource requirement of the Datacenter’s servers in order to reserve their right quantities at the right time and thus avoid either the waste caused by their over-supplying or the performance problems caused by their under-supplying. In this article, we explored the performance of ARMA models in the realization of this type of prediction. It appears that with good selection of parameters, the ARMA models produce reliable predictions but also about 30% higher than those performed with naive methods. These results could be used to feed virtual machine management algorithms into Cloud Datacenters, particularly in the decision-making of their placement or migration for the rationalization of provisioned resources.
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Gbaguidi, F.A.R., Boumerdassi, S., Milocco, R., Ezin, E.C. (2019). Adaptive ARMA Based Prediction of CPU Consumption of Servers into Datacenters. In: Renault, É., Boumerdassi, S., Bouzefrane, S. (eds) Mobile, Secure, and Programmable Networking. MSPN 2018. Lecture Notes in Computer Science(), vol 11005. Springer, Cham. https://doi.org/10.1007/978-3-030-03101-5_23
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DOI: https://doi.org/10.1007/978-3-030-03101-5_23
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