Efficient and effective multi-objective optimization for real-time multi-task systems

Embedded real-time multi-task systems must often not only comply with timing constraints but also need to meet energy requirements. However, optimizing energy consumption might lead to higher Worst-Case Execution Time (WCET), leading to an un-schedulable system, as frequently executed code can easily differ from timing-critical code. To handle such an impasse in this paper, we formulate a Metaheuristic Algorithm-based Multi-objective Optimization (MAMO) for multi-task real-time systems. But, performing multiple WCET, energy, and schedulability analyses to solve a MAMO poses a bottleneck concerning compilation times. Therefore, we propose two novel approaches - Path-based Constraint Approach (PCA) and Impact-based Constraint Approach (ICA) - to reduce the solution search space size and to cope with this problem. Evaluations showed that PCA and ICA reduced compilation times by 85.31% and 77.31%, on average, over MAMO. For all the task sets, out of all solutions found by ICA-FPA, on average, 88.89% were on the final Pareto front.

Subjects

Compilers

Design space reduction

Metaheuristic algorithms

Multi-objective optimization

Real-time systems

DDC Class

004: Computer Sciences

510: Mathematics

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

OASIcs-WCET-2023-5.pdf

Type

Main Article

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768.21 KB

Format

Adobe PDF

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Efficient and effective multi-objective optimization for real-time multi-task systems