SLA-based energy aware scheduling of precedence-constrained applications on DVFS-enabled clusters

The energy aware scheduling problem has been a critical issue in high-performance clusters owing to their high operation cost, environmental impact, and low reliability. An existing technique to reduce energy consumption of applications is dynamic voltage/frequency scaling (DVFS). In this paper, we develop an energy aware scheduling algorithm called EASLA for precedence-constrained applications in the context of Service Level Agreement (SLA) on DVFS-enabled cluster systems. Due to the dependencies among tasks and makespan extension, there may be some slacks under used. The main idea of the EASLA algorithm is to distribute each slack to a set of tasks and scale frequencies down to try to minimize energy consumption. Specifically, it first finds the maximum set of independent tasks for each task, and then iteratively allocates each slack to the maximum independent set whose total energy reduction is the maximal. Randomly generated graphs and two real-world applications are tested in our experiments. The experimental results show that our scheduling algorithm can save up to 22.68% and 12.01% energy consumption compared with GreedyDVS and EvenlyDVS algorithms, respectively.