QLSA-MOEAD integration for precision task scheduling in heterogeneous computing environments

Heterogeneous computing infrastructures integrating CPUs, GPUs, and FPGAs present critical challenges in efficient task scheduling due to hardware diversity, complex task dependencies, and conflicting optimization objectives. This work formulates workflow scheduling as a multi-objective optimization problem that minimizes makespan and maximizes resource utilization. For synthetic benchmarks (FFT, Molecular), the approach minimizes makespan and maximizes resource utilization. For the CyberShake seismic workflow, energy consumption is added as a third objective. This research proposes QLSA-MOEAD, a hybrid framework combining three complementary mechanisms: Q-learning for intelligent initialization, Simulated Annealing for local refinement, and MOEA/D for multi-objective decomposition. This integration balances exploration and exploitation effectively. Comprehensive evaluations on 20 test cases (structured FFT, unstructured molecular, and real-world CyberShake workflows) show superior performance. QLSA-MOEAD achieves the best solution quality in 14 out of 16 FFT/molecular cases and outperforms all baselines on CyberShake. A large-scale Montage workflow (100 tasks, 179 dependencies) validates scalability under real-time task arrivals. The framework maintains excellent convergence and diversity across different CCR levels. Q-learning achieves fast decision-making with 0.80-1.70 ms response time. Statistical validation (Wilcoxon and Friedman tests), ablation studies, and parameter sensitivity analysis confirm framework robustness. These results establish QLSA-MOEAD as an effective solution for both static and dynamic workflow scheduling in heterogeneous environments.