Latency Aware Task Allocation in Heterogeneous Multi-Core System using Whale Optimization Algorithm

Heterogeneous multi-core systems are increasingly used in latency-sensitive computing environments because they combine different processing units such as high-performance CPUs, low-power cores, GPUs, NPUs, and DSPs. However, the diversity of these processing units makes task allocation difficult, especially when execution time, communication delay, processor availability, energy consumption, and workload dependencies must be considered together. This study develops a latency-aware task allocation framework for heterogeneous multi-core systems using the Whale Optimization Algorithm (WOA). The objective is to formulate task scheduling as a discrete task-to-core mapping problem and evaluate the effectiveness of WOA under independent, DAG-based, real-time, and energy-aware workload conditions. The proposed method adapts the continuous WOA into a discrete integer-encoded scheduling model, where each whale represents a complete task-to-processor assignment. A latency-based fitness function was used for WOA-Latency, while an extended weighted objective was applied for WOA-EnergyAware to examine latency-energy trade-offs. The experimental evaluation compared the proposed WOA variants with Random, Round-Robin, MET, MCT, Min-Min, Max-Min, HEFT, PEFT, GA, PSO, GWO, ACO, and a WOA-MCT hybrid scheduler. The evaluation included repeated simulation runs, statistical reporting, convergence analysis, scalability testing, deadline miss analysis, and parameter sensitivity assessment. The results show that WOA-Latency outperforms several naïve and metaheuristic baselines in some workload settings, particularly Random, Round-Robin, GA, PSO, and GWO. However, deterministic heuristics such as MCT and Min-Min performed better for independent-task workloads, while HEFT and PEFT achieved stronger results for DAG-based scheduling. The energy-aware WOA variant demonstrated the framework’s potential for multi-objective optimisation, although with some latency trade-off. Overall, the study concludes that WOA is a flexible and extensible optimisation framework for heterogeneous task allocation, especially in complex, nonlinear, energy-aware, and multi-objective scheduling scenarios.

Heterogeneous multi-core systems; task scheduling; Whale Optimization Algorithm; latency-aware allocation; energy-aware scheduling.

1. Abdullahi, M., Ngadi, M. A., Abdulhamid, S. M., & Ahmad, B. I. (2020). Symbiotic organism search optimization-based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640–650. [Google Scholar] [Crossref]

2. Arabnejad, H., & Barbosa, J. G. (2014). List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems, 25(3), 682–694. [Google Scholar] [Crossref]

3. Borkar, S., & Chien, A. A. (2011). The future of microprocessors. Communications of the ACM, 54(5), 67–77. [Google Scholar] [Crossref]

4. Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L., Maheswaran, M., Reuther, A. I., Robertson, J. P., Theys, M. D., & Yao, B. (2001). A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 61(6), 810–837. [Google Scholar] [Crossref]

5. Chen, H., Wang, X., & Li, Y. (2020). Hybrid metaheuristic scheduling for heterogeneous computing systems. IEEE Access, 8, 123456–123470. [Google Scholar] [Crossref]

6. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (pp. 1942–1948). [Google Scholar] [Crossref]

7. Kwok, Y. K., & Ahmad, I. (1999). Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys, 31(4), 406–471. [Google Scholar] [Crossref]

8. Li, K., Xu, G., Zhao, G., Dong, Y., & Wang, D. (2018). Cloud task scheduling based on load balancing. IEEE Transactions on Cloud Computing, 6(3), 709–720. [Google Scholar] [Crossref]

9. Mittal, S. (2016). A survey of techniques for improving energy efficiency in embedded computing systems. International Journal of Computer Aided Engineering and Technology, 8(4), 440–459. [Google Scholar] [Crossref]

10. Mirjalili, S. (2019). Evolutionary algorithms and neural networks. Springer. [Google Scholar] [Crossref]

11. Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. [Google Scholar] [Crossref]

12. Sahoo, B., Mohanty, S., & Patra, M. R. (2022). Multi-objective task scheduling for heterogeneous computing systems using evolutionary algorithms. Future Generation Computer Systems, 129, 116–131. [Google Scholar] [Crossref]

13. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. [Google Scholar] [Crossref]

14. Singh, A., Kumar, R., & Singh, P. (2021). Energy-efficient scheduling in heterogeneous computing systems. Journal of Systems Architecture, 115, 101977. [Google Scholar] [Crossref]

15. Tang, M., Chen, Y., & Li, X. (2022). Latency-aware resource allocation in edge computing. IEEE Transactions on Services Computing, 15(2), 987–1001. [Google Scholar] [Crossref]

16. Topcuoglu, H., Hariri, S., & Wu, M. Y. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274. [Google Scholar] [Crossref]

17. Wang, S., Liu, Z., & Chen, X. (2023). Multi-objective optimization for heterogeneous task scheduling. Journal of Parallel and Distributed Computing, 176, 45–60. [Google Scholar] [Crossref]

18. Xu, X., Li, Y., & Zhang, J. (2021). Deep learning-based scheduling in heterogeneous computing [Google Scholar] [Crossref]

19. Zhao, L., Sakellariou, R., & Tsiakkouri, E. (2020). Latency-aware scheduling strategies for heterogeneous computing systems. Journal of Parallel and Distributed Computing, 142, 1–12. [Google Scholar] [Crossref]