Dynamic resource allocation using performance forecasting

To benefit from the performance gains and cost savings enabled by elasticity in cloud IaaS environments, effective automated mechanisms for scaling are essential. This automation requires monitoring system status and defining criteria to trigger allocation and deallocation of resources. While these criteria are usually based upon resource utilization, this may be inadequate due to the impossibility of identifying the actual amount of required resources. Consequently, most systems update resource allocation in small increments, e.g., one VM at a time, which may negatively affect performance and cost. In this paper, we propose a novel approach in which the system monitors workload, instead of utilization, and, by means of a scalability model, it makes predictions of resource demand and updates the allocated resources accordingly. We provide an implementation of this approach and describe experimental results that show its effectiveness.