A resource leasing policy for on-demand computing

Leasing computational resources for on-demand computing is now a viable option for providers of network services. Temporary spikes or lulls in demand for a service can be accommodated by flexible leasing arrangements. From the service provider's perspective, the problem is how many resources to lease and for how long. In this paper we formulate and solve the resource leasing problem for the case of a single service. The objective is to minimize the cost of leasing resources while still maintaining an adequate quality of service, which we measure by the average wait time of requests. Demand for the service and execution times of service requests are modeled as random variables. The problem is formulated as a continuous-time, infinite-horizon Markov decision problem. We use the dynamic programming method of value iteration for its solution and we characterize the resulting optimal cost function. We find that the cost of providing a service is convex-like in the number of resources leased and nondecreasing in the number of requests in the system. Close examination of the optimal cost function shows that the cost of providing a service is more sensitive to underdeployment than to overdeployment. Thus, when demand for the service is known to exist, but is unpredictable, it is better to lease more resources than fewer resources.