A Stochastic Approach for Virtual Machine Placement in Volunteer Cloud Federations

Abstract

Volunteer cloud federations (VCFs) are cloud federations where clouds may join and leave a federation without restrictions and may contribute resources to the federation without long term commitment. This makes it difficult to predict the long term availability of resources. Also, in IaaS VCFs, volunteers may collectively contribute a large number of heterogeneous virtual machine instances. In this paper, we focus on the problem of efficiently allocating this dynamic, heterogeneous capacity to a flow of incoming VM instantiation requests. We propose an approach, called stochastic least differential capacity (SLDC),that allows over-provisioning only when necessary. The approach uses historical information about recent instantiation requests to derive stochastic predictions regarding future demand. We implemented VCFSim, a VCF simulator that uses the proposed resource allocation solution. The results of the experimental evaluation show that the proposed approach is able to improve the success rate of VM instantiation requests by up to 38%compared to an approach that uses exact matching with no demand forecasting.