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An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

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Abstract

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

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Acknowledgments

The authors wish to thank GIK Institute for providing research facilities. This work was sponsored by the GIK Institute graduate research fund under GA-1 scheme.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. The Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Swabi, Pakistan

    Muhammad Sulaiman & Zahid Halim

  2. Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan

    Muhammad Sulaiman

  3. Computer Science Laboratory of Paris-Nord, Sorbonne University, Paris, France

    Mustapha Lebbah

  4. Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Muhammad Waqas & Shanshan Tu

  5. Faculty of Computer Science and Engineering, GIK, Institute of Engineering Sciences and Technology, Swabi, Pakistan

    Muhammad Waqas

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  1. Muhammad Sulaiman
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Correspondence to Zahid Halim.

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Sulaiman, M., Halim, Z., Lebbah, M. et al. An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment. J Grid Computing 19, 11 (2021). https://doi.org/10.1007/s10723-021-09552-4

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