Energy aware DAG scheduling on heterogeneous systems

Abstract

We address the problem of scheduling directed a-cyclic task graph (DAG) on a heterogeneous distributed processor system with the twin objectives of minimizing finish time and energy consumption. Previous scheduling heuristics have assigned DAGs to processors to minimize overall run-time of the application. But applications on embedded systems, such as high performance DSP in image processing, multimedia, and wireless security, need schedules which use low energy too.We develop a new scheduling algorithm called Energy Aware DAG Scheduling (EADAGS) on heterogeneous processors that can run on discrete operating voltages. Such processors can scale down their voltages and slow down to reduce energy whenever they idle due to task dependencies. EADAGS combines dynamic voltage scaling (DVS) with Decisive Path Scheduling (DPS) to achieve the twin objectives. Using simulations we show average energy consumption reduction over DPS by 40%. Energy savings increased with increasing number of nodes or increasing Communication to Computation Ratios and decreased with increasing parallelism or increasing number of available processors. These results were based on a software simulation study over a large set of randomly generated graphs as well as graphs for real-world problems with various characteristics.

Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems

“…The authors considered continuous voltage. In [13] the authors combined the Decisive Path Scheduling (DPS) list scheduling algorithm and dynamic voltage scaling with dynamic power technique.…”

Energy Aware Scheduling based on Two-phase Frequency Scaling for Parallel Tasks in Cluster

“…The slack time can be classified into two categories [12]: Worst Slack Time (WST) and Workload-variation Slack Time (WVST). WST belongs to static slack time, which results from low processor utilization due to precedence constraints between tasks.…”

Optimizing Data-Accessing Energy Consumption for Workflow Applications in Clouds

“…To compare the performance of our MECDS algorithm with other ones, we adapt four other existing scheduling algorithms including HEFT [30], MMF-DVFS [31], ECS+idle [32], and EADAGS [11]. Among the four algorithms, HEFT is the only one without energy-aware functionality, and we use it as the baseline for performance comparison.…”

“…As more and more workflows have been deployed on cloud platforms, energy-aware scheduling for workflow applications attracts plenty of attentions recently [6][7][8][10][11][12][13][14]. Most workflow applications are data-intensive and consist of many computational tasks with constraint to data-flow dependencies.…”