Abstract:
The concern of reducing power consumption and the
schedule length (or makespan) has added new levels of complexities to the scheduling of task graphs on multiprocessors. Recent
researches in the field have obtained considerable improvement in
power consumption with a comparable makespan by exploring
a large search space with mixed integer linear programming
(MIP). However, MIP based solutions are applicable only to
smaller instances of task graphs. In this paper, we propose a
multi-objective genetic algorithm (P2Gen) that uses dynamic
voltage/frequency scaling (DVFS) to perform Power (P) and
Performance (P) aware scheduling. With DVFS, tasks are made to
run on low voltages, which decreases their computation power.
However, it also increases their execution costs and hence, is
optimized with a genetic algorithm. In the first phase, P2Gen
optimizes the makespan whereas the second phase uses DVFS
with multiple tolerance levels of the obtained makespan to
optimize both the objectives. To enhance the convergence of
P2Gen as well as exploring a variety of population, each task
is run by integrating the maximum and minimum voltage on a
processor instead of iterating through all the voltage levels. The
results demonstrate that P2Gen is able to balance power and
performance better than other algorithms.
