Abstract
Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. In this context, monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we investigate how we can predict the performance of a service to dynamically adjust allocated resources based on predictions. In other words, instead of “repairing” because a threshold has been crossed, we attempt to stay ahead and allocate an optimized amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments.
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This research is supported by CAPES - process BEX-1110-/14-4.
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Moura, P., Kon, F., Voulgaris, S., van Steen, M. (2015). Using Performance Forecasting to Accelerate Elasticity. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2015. Lecture Notes in Computer Science(), vol 9438. Springer, Cham. https://doi.org/10.1007/978-3-319-28448-4_2
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DOI: https://doi.org/10.1007/978-3-319-28448-4_2
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