Risk Aware Stochastic Placement of Cloud Services

Abstract

Allocating the right amount of resources to each service in any of the datacenters in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, or run into situation where the allocated local resources are insufficient to support the current demand. In these cases, the service providers deploy overflow mechanisms such as redirecting traffic to a remote datacenter or temporarily leasing additional resources (at a higher price) from the cloud infrastructure owner. The additional cost is in many cases proportional to the amount of overflow demand. In this paper we study this approach and develop a novel mechanism to assign services to datacenters based on the available resources in each datacenter and the distribution of the demand for each service. We use comprehensive analysis to prove that the overall overflow cost is almost optimal for arbitrary demand distributions, as long as there are no dependencies among the services. We further show, using simulation based on real data that the scheme performs very well on realistic service workloads.