Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service

In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.