Abstract
Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.
Highlights
Multiprocessor systems can achieve higher performance by thread-level parallelism with a lower clock frequency, which have advantages over highly superscalar uniprocessor architectures [1].Multiprocessor systems can be classified into homogeneous and heterogeneous ones
Another drawback of Shuffled Frog Leaping Algorithm (SFLA) applied to Energy-aware Real-time Tasks Scheduling Problem (e-RTSP) is that it is easy to fall into a local optimal solution
The Energy-Aware Real-Time Tasks Scheduling Problem for heterogeneous processor systems named e-RTSP is formulated as a combinatorial optimization problem
Summary
Multiprocessor systems can achieve higher performance by thread-level parallelism with a lower clock frequency, which have advantages over highly superscalar uniprocessor architectures [1]. A dynamic programming method is proposed to provide certain worst-case performance guarantee [10] These approximate or heuristic algorithms cannot efficiently work in practice for large size problems, these studies enlighten the way. The main objective of this work is to design novel real-time scheduling algorithms based on SFLA, which can quickly find an optimal solution satisfying hard task deadlines and reducing energy consumption. Formulate the energy-aware real-time taskscheduling problem for HMS by incorporating the energy consumption into the constraints and optimization objectives.
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