Priority-based scheduling for large-scale distribute systems with energy awareness

Large-scale distributed computing systems (LDSs), such as grids and clouds are primarily designed to provide massive computing capacity. These systems dissipate often excessive energy to both power and cool them. Concerns over greening these systems have prompted a call for scheduling policies with energy awareness (e.g., energy proportionality). The dynamic and heterogeneous nature of resources and tasks in LDSs is a major hurdle to be overcome for energy efficiency when designing scheduling policies. In this paper, we address the problem of scheduling tasks with different priorities (deadlines) for energy efficiency exploiting resource heterogeneity. Specifically, our investigation for energy efficiency focuses on two issues: (1) balancing the workload in the way utilization is maximized and (2) power management by controlling execution of tasks on processor for ensuring the energy is optimally consumed. We form a hierarchical scheduler that exploits the multi-core architecture for effective scheduling. Our scheduling approach exploits the diversity of task priority for proper load balancing across heterogeneous processors while observing energy consumption in the system. Simulation experiments prove the efficacy of our approach, and the comparison results indicate our scheduling policy helps improve energy efficiency of the system.