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Efficient and Effective Multi-Objective Optimization for Real-Time Multi-Task Systems

Authors Shashank Jadhav , Heiko Falk

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Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering → Compilers
  • Mathematics of computing → Discrete mathematics
Keywords
  • Real-time systems
  • Multi-objective optimization
  • Metaheuristic algorithms
  • Compilers
  • Design space reduction

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Abstract

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.

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Shashank Jadhav and Heiko Falk. Efficient and Effective Multi-Objective Optimization for Real-Time Multi-Task Systems. In 21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023). Open Access Series in Informatics (OASIcs), Volume 114, pp. 5:1-5:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/OASIcs.WCET.2023.5

Author Details

Shashank Jadhav
  • Hamburg University of Technology, Germany
Heiko Falk
  • Hamburg University of Technology, Germany

Funding

This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779882.

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