Energy consumption is one of the most important practical and timely problem associated with data centers for cloud computing. The urgency of this problem has been exposed by both, the governmental agencies and the industry. While policies that consider the physical design of the data centers have been studied in the technical literature rather thoroughly, the operational characteristics of the data centers (e.g., the required performance of the executed applications, such as the type of services and the applications being supported, their QoS requirements, associated background server maintenance processes, etc.) have rarely been accounted for. In particular, there is a need to investigate the implications of exploiting the operational characteristics of the data centers in the context of power management policies, as those could lead to potentially significant energy savings and operational cost reduction of a computer cloud. Intellectual Merit: In the proposed project, we will study the effect of the characteristics of the applications and their requirements on the design of the data centers? power management policies. A particular interest of our study will be the design of such policies in the context of distributed data centers, as provided by the cloud-computing paradigm. In particular, we will investigate the problem of Power Management Policies in heterogeneous data centers for the case of independent/individual data centers from the perspective of job profiling, such as failure and latency tolerance. We will study the possibility of decomposition of the peak loads to a data center into high latency tolerant requests that needs to be migrated to different, less busy data centers, or high failure tolerant requests that require less server resources being activated at peak times. Similarly, we will study the approach of grouping jobs, as to reduce the demand variability on servers. Such decomposition and grouping will be dynamically performed. Broader Impacts: The results are expected to significantly improve over the other approaches of power management of data centers, as well as represent estimations which closer match practical systems. The educational impact includes training of graduate and undergraduate students and outreach activities.
