Interconnection Network Energy-Aware Workflow Scheduling Algorithm on Heterogeneous Systems

Abstract

Heterogeneous systems based on multicore (CPU) and manycore (GPU) processors have been regarded as an important computing infrastructure in recent years. Large-scale computationally intensive scientific workflow applications have recently been deployed on such systems. However, improving the system performance and reducing the energy consumption under user deadline constraints remain challenging problems. In this study, we first investigate the computing node network energy consumption problem of fat-tree interconnection networks for a low communication to computation ratio workflow application. We then propose a heuristic list-based network energy-efficient workflow scheduling algorithm (NEEWS) including top-level task computing, task subdeadline initialization, a dynamic adjustment, and an edge data optimization communication method. Extensive simulations were conducted based on randomly generated workflow applications and two real-world scientific applications. The experiment results clearly demonstrate that our proposed workflow scheduling strategy outperforms three other algorithms in terms of energy consumption. In particular, NEEWS is extremely suitable owing to its high parallelism and low communication in large-scale scientific applications.