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Parallel Ant Colony Optimization for Scheduling Independent Tasks

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Innovations in Bio-Inspired Computing and Applications (IBICA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 649))

Abstract

Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One such approach is Ant Colony Optimization (ACO) which has a potential to benefit from a parallel execution. In this article, we propose two new scheduling methods based on parallel ACO to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan by both parallel ACO variants.

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Authors and Affiliations

  1. Chemnitz University of Technology, Chemnitz, Germany

    Robert Dietze & Maximilian Kränert

Authors
  1. Robert Dietze
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  2. Maximilian Kränert
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Corresponding author

Correspondence to Robert Dietze .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, Flame University, Pune, Maharashtra, India

    Ajith Abraham

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Interdisciplinary Studies Research Center (ISRC), Institute of Engineering, Polytechnique of Porto (ISEP/P.PORTO), INOV (Institute for Systems and Computer Engineering, Technology and Science), Porto, Portugal

    Ana Maria Madureira

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Türkiye

    Cengiz Kahraman

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dietze, R., Kränert, M. (2023). Parallel Ant Colony Optimization for Scheduling Independent Tasks. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_34

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