Summary: EnergyAware Partitioning for Multiprocessor RealTime Systems
Hakan Aydin, Qi Yang
Computer Science Department
George Mason University
Fairfax, VA 22030
In this paper, we address the problem of partitioning peri
odic realtime tasks in a multiprocessor platform by consid
ering both feasibility and energyawareness perspectives: our
objective is to compute the feasible partitioning that results in
minimum energy consumption on multiple identical processors
by using variable voltage EarliestDeadlineFirst scheduling.
We show that the problem is NPHard in the strong sense on
m # 2 processors even when feasibility is guaranteed a priori.
Then, we develop our framework where load balancing plays
a major role in producing energyefficient partitionings. We
evaluate the feasibility and energyefficiency performances of
partitioning heuristics experimentally.