A Capacity Allocation Approach for Volunteer Cloud Federations Using Poisson-Gamma Gibbs Sampling

In volunteer cloud federations (VCFs), volunteers join and leave without restrictions and may collectively contribute a large number of heterogeneous virtual machine instances. A challenge is to efficiently allocate this dynamic, heterogeneous capacity to a flow of incoming virtual machine (VM) instantiation requests, i.e., maximize the number of virtual machines that may be placed on the VCF. Cloud federations may allocate VMs far more efficiently if they can accurately predict the demand in terms of VM instantiation requests. In this paper, we present a stochastic technique that forecasts future demand to efficiently allocate VMs to VM instantiation requests. Our approach uses a Markov Chain Monte Carlo (MCMC) simulation known as the Poisson-Gamma Gibbs (PGG) sampler. The PGG sampler is used to determine the arrival rate of each type of VM instantiation requests. This arrival rate is then used to determine an optimal VM placement for the incoming VM instantiation requests. We compared our approach to a solution that adopts a static smallest-fit approach. The experimental results showed that our solution reacts quickly to abrupt changes in the frequency of VM instantiation requests and performs 10% better than the static smallest-fit approach in terms of the total number of satisfied requests.