vPnP: Automated coordination of power and performance in virtualized datacenters

Both power and performance are important issues in today's datacenters. It is hard to achieve optimization in both aspects on shared infrastructures due to system dynamics. Previous work mostly emphasized on either aspect or relied on models that were trained off-line for specific workload. In this paper, we present vPnP, a feedback control-based coordination system that provides guarantees on a service level agreement with respect to performance and a power budget in virtualized environments. This system can adapt gracefully to workload change. It consists of two self-tuning model predictors and a utility function optimizer. The predictors correlate system resource allocation to power and performance, respectively. The optimizer finds the optimal solution for a tradeoff between power and performance. Experimental results using TPC-W benchmark show vPnP can achieve different levels of tradeoff in a more flexible way than an existing two-layer feedback control approach. More importantly, vPnP shows its robustness over a variety of workloads. It reduces performance relative deviation by 17% compared with the two-layer feedback controller.