Host load prediction in a Google compute cloud with a Bayesian model
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Event aware workload prediction: a study using auction events
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
An approach for constructing private storage services as a unified fault-tolerant system
Journal of Systems and Software
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Google hostload prediction based on Bayesian model with optimized feature combination
Journal of Parallel and Distributed Computing
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Infrastructures serving on-line applications experience dynamic workload variations depending on diverse factors such as popularity, marketing, periodic patterns, fads, trends, events, etc. Some predictable factors such as trends, periodicity or scheduled events allow for proactive resource provisioning in order to meet fluctuations in workloads. However, proactive resource provisioning requires prediction models forecasting future workload patterns. This paper proposes a multi-model prediction approach, in which data are grouped into bins based on content locality, and an autoregressive prediction model is assigned to each locality-preserving bin. The prediction models are shown to be identified and fitted in a computationally efficient way. We demonstrate experimentally that our multi-model approach improves locality over the uni-model approach, while achieving efficient resource provisioning and preserving a high resource utilization and load balance.