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.

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