Dynamic Resource Allocation for Spot Markets in Clouds

Cloud computing promises on-demand provisioning of resource to applications and services. To deal with dynamically fluctuating resource demands, market-driven resource allocation has been proposed and recently implemented by commercial cloud providers like Amazon EC2. In this environment, cloud resources are offered in distinct types of virtual machines (VMs) and the cloud provider runs a continuous market-driven mechanism for each VM type with the goal of achieving maximum revenue over time. However, as demand of each VM type can fluctuate independently at run time, it becomes a challenging problem to dynamically allocate data center resources to each spot market to maximize cloud provider’s total revenue. In this paper, we present a solution to this problem that consists of 2 parts: (1) market analysis for forecasting the demand for each spot market, and (2) a dynamic scheduling and consolidation mechanism that allocate resource to each spot market to maximize total revenue. As optimally allocating resources for revenue maximization is a NP-hard problem, we show our algorithms can approximate the optimal solutions to this problem under both fixed and variable pricing schemes. Simulation studies confirm the effectiveness of our approach.