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We study the problem of maximizing the average hourly profit earned by aSoftware-as-a-Service (SaaS) provider who runs a software service on behalf ofa customer using servers rented from an Infrastructure-as-a-Service (IaaS)provider. The SaaS provider earns a fee per successful transaction and incurscosts proportional to the number of server-hours it uses. A number of resourceallocation policies for this or similar problems have been proposed in previouswork. However, to the best of our knowledge, these policies have not beencomparatively evaluated in a cloud environment. This paper reports on anempirical evaluation of three policies using a replica of Wikipedia deployed onthe Amazon EC2 cloud. Experimental results show that a policy based on asolution to an optimization problem derived from the SaaS provider's utilityfunction outperforms well-known heuristics that have been proposed for similarproblems. It is also shown that all three policies outperform a "reactive"allocation approach based on Amazon's auto-scaling feature.

Squeezing out the Cloud via Profit-Maximizing Resource Allocation Policies