An Analytical Model for Estimating Cloud Resources of Elastic Services

In the cloud, ensuring proper elasticity for hosted applications and services is a challenging problem and far from being solved. To achieve proper elasticity, the minimal number of cloud resources that are needed to satisfy a particular service level objective (SLO) requirement has to be determined. In this paper, we present an analytical model based on Markov chains to predict the number of cloud instances or virtual machines (VMs) needed to satisfy a given SLO performance requirement such as response time, throughput, or request loss probability. For the estimation of these SLO performance metrics, our analytical model takes the offered workload, the number of VM instances as an input, and the capacity of each VM instance. The correctness of the model has been verified using discrete-event simulation. Our model has also been validated using experimental measurements conducted on the Amazon Web Services cloud platform.