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Electronics 2017, 6(2), 40; doi:10.3390/electronics6020040

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

1
Computer Science Department, University of Bahrain, Sakhir, Bahrain
2
Computer Engineering Department, University of Bahrain, Sakhir, Bahrain
*
Author to whom correspondence should be addressed.
Academic Editor: Mostafa Bassiouni
Received: 4 April 2017 / Revised: 27 April 2017 / Accepted: 15 May 2017 / Published: 19 May 2017
View Full-Text   |   Download PDF [2526 KB, uploaded 19 May 2017]   |  

Abstract

Minimizing power consumption to prolong battery life has become an important design issue for portable battery-operated devices such as smartphones and personal digital assistants (PDAs). On a Dynamic Voltage Scaling (DVS) enabled processor, power consumption can be reduced by scaling down the operating frequency of the processor whenever the full processing speed is not required. Real-time task scheduling is a complex and challenging problem for DVS-enabled multiprocessor systems. This paper first formulates the real-time task scheduling for DVS-enabled multiprocessor systems as a combinatorial optimization problem. It then proposes a genetic algorithm that is hybridized with the stochastic evolution algorithm to allocate and schedule real-time tasks with precedence constraints. It presents specialized crossover and perturb operations as well as a topology preserving algorithm to generate the initial population. A comprehensive simulation study has been done using synthetic and real benchmark data to evaluate the performance of the proposed Hybrid Genetic Algorithm (HGA) in terms of solution quality and efficiency. The performance of the proposed HGA has been compared with the genetic algorithm, particle swarm optimization, cuckoo search, and ant colony optimization. The simulation results show that HGA outperforms the other algorithms in terms of solution quality. View Full-Text
Keywords: multiprocessor systems; task-allocation; task scheduling; real-time systems; genetic algorithm; power-aware task scheduling; hybridization multiprocessor systems; task-allocation; task scheduling; real-time systems; genetic algorithm; power-aware task scheduling; hybridization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mahmood, A.; Khan, S.A.; Albalooshi, F.; Awwad, N. Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm. Electronics 2017, 6, 40.

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