Hybrid resource provisioning for clouds

Abstract

Flexible resource provisioning, the assignment of virtual machines (VMs) to physical machine, is a key requirement for cloud computing. To achieve "provisioning elasticity", the cloud needs to manage its available resources on demand. A-priori, static, VM provisioning introduces no runtime overhead but fails to deal with unanticipated changes in resource demands. Dynamic provisioning addresses this problem but introduces runtime overhead. To reduce VM management overhead so more useful work can be done and to also avoid sub-optimal provisioning we propose a hybrid approach that combines static and dynamic provisioning. The idea is to adapt a good initial static placement of VMs in response to evolving load characteristics, using live migration, as long as the overhead of doing so is low and the effectiveness is high. When this is no longer so, we trigger a revised static placement. (Thus, we are essentially applying local multi-objective optimization to tune a global optimization with reduced overhead.) This approach requires a complicated migration decision algorithm based on current and predicted:future workloads, power consumptions and memory usage in the host machines as well as network burst characteristics for the various possible VM multiplexings (combinations of VMs on a host). A further challenge is to identify those characteristics of the dynamic provisioning that should trigger static re-provisioning.