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.