Squeezing out the cloud via profitmaximizing resource allocation policies

We study the problem of maximizing the aver-age hourly profit earned by a Software-as-a-Service (SaaS) provider who runs a software service on be-half of a customer using servers rented from an Infrastructure-as-a-Service (IaaS) provider. The SaaS provider earns a fee per successful transac-tion and incurs costs proportional to the number of server-hours it uses. A number of resource alloca-tion policies for this or similar problems have been proposed in previous work. However, to the best of our knowledge, these policies have not been compar-atively evaluated in a cloud environment. This paper reports on an empirical evaluation of three policies using a replica of Wikipedia deployed on the Amazon EC2 cloud. Experimental results show that a policy based on a solution to an optimization problem de-rived from the SaaS provider’s utility function out-performs well-known heuristics that have been pro-posed for similar problems. It is also shown that all three policies outperform a “reactive ” allocation ap-proach based on Amazon’s auto-scaling feature.