Energy-Aware Task Partitioning on Heterogeneous Multiprocessor Platforms
Efficient task partitioning plays a crucial role in achieving high
performance at multiprocessor platforms. This paper addresses
the problem of energy-aware static partitioning of periodic realtime
tasks on heterogeneous multiprocessor platforms. A Particle
Swarm Optimization variant based on Min-min technique for
task partitioning is proposed. The proposed approach aims to
minimize the overall energy consumption, meanwhile avoid
deadline violations. An energy-aware cost function is proposed
to be considered in the proposed approach. Extensive simulations
and comparisons are conducted in order to validate the
effectiveness of the proposed technique. The achieved results
demonstrate that the proposed partitioning scheme significantly
surpasses previous approaches in terms of both number of
iterations and energy savings.
Keywords: Task Partitioning, Task Assignment, Heterogeneous
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ABOUT THE AUTHORS
Elsayed Saad
Electronics, Communication and Computer Engineering Department, Helwan University
Medhat Awadalla
Electrical and Computer Engineering Department, Sultan Qaboos University
Mohamed Shalan
Computer Science and Engineering Department, the American University in Cairo
Abdullah Elewi
Electronics, Communication and Computer Engineering Department, Helwan University
Elsayed Saad
Electronics, Communication and Computer Engineering Department, Helwan University
Medhat Awadalla
Electrical and Computer Engineering Department, Sultan Qaboos University
Mohamed Shalan
Computer Science and Engineering Department, the American University in Cairo
Abdullah Elewi
Electronics, Communication and Computer Engineering Department, Helwan University
