Energy Management of Cloud Data Center Using Neural Networks

The cost effective deployment of applications into cloud has resulted in significant increase of cloud based services. This has in turn led to large number of data centers in delivering such services at scale, offering myriad of user experiences and minimal downtime. Such commitments of providing differentiated services at scale, invites the necessity to manage energy and performance of constituting nodes in data centers without impacting service level agreements (SLAs). Ensuring energy efficiency in these data centers is a major problem in cloud computing. Many optimization policies like workload consolidation, machine placement etc. helps in containing the energy requirement of servers in data centers. In this paper, we introduce a data-driven prognostic neural network based framework that will consider power consumed by all the components in server beyond incoming request load and effectively forecast it at any point in future.