Enabling autonomic power-aware management of instrumented data centers

Sensor networks support flexible, non-intrusive and fine-grained data collection and processing and can enable online monitoring of data center operating conditions as well as autonomic data center management. This paper describes the architecture and implementation of an autonomic power aware data center management framework, which is based on the integration of embedded sensors with computational models and workload schedulers to improve data center performance in terms of energy consumption and throughput. Specifically, workload schedulers use online information about data center operating conditions obtained from the sensors to generate appropriate management policies. Furthermore, local processing within the sensor network is used to enable timely responses to changes in operating conditions and determine job migration strategies. Experimental results demonstrate that the framework achieves near optimal management, and in-network analysis enables timely response while reducing overheads.