Using performance forecasting to accelerate elasticity

Abstract. Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. 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 ask ourselves how we can predict the performance of a service in order 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 a best 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