Runtime Demand Estimation for effective dynamic resource management

Systems management techniques that allocate resources to running entities, such as processes and virtual machines (VMs), often require estimates of the resources required by each of these resource consumers. For example, many proposed virtual machine placement algorithms attempt to allocate VMs to physical hosts in such a way as to minimize the number of physical hosts that are occupied, while ensuring that each VM receives the CPU required to do its task adequately. The common practice is to assume that the CPU requirement is equal to the current CPU utilization, or to use a prediction of it over an appropriate time horizon. In this paper, we demonstrate that, when multiple VMs or processes co-reside on a physical host, the measured CPU utilization may provide a poor estimate of the actual requirement. We derive a simple, much more accurate alternative estimate of CPU demand, implement it, and demonstrate its superiority experimentally. Furthermore, we demonstrate that using our demand estimation framework in conjunction with dynamic resource allocation in a virtualized environment greatly improves the effectiveness of dynamic placement, resulting in one-shot convergence to optimal placement and significant improvements in the overall performance of the individual VMs.