TL;DR
MO-UDRL-H is presented, a multi-objective uncertain deep reinforcement learning scheduler with a Hybrid Pareto Safety Shield that achieves the highest mean Pareto hypervolume and the lowest GD and Spacing among the applicable multi-solution methods.
Energy-aware task scheduling in fog–cloud IoT networks must balance monetary cost, deadlines and energy. We propose hybrid grey wolf optimizer-reinforcement learning (HGWO-RL), which combines a grey wolf optimizer (GWO) with a tabular Q-learning agent that selects move operators to refine the three elite solutions. The...
INTRODUCTION: Edge-cloud schedulers must coordinate latency, energy, load balance, and deadline compliance under changing demand while keeping task-arrival and throughput units physically consistent.OBJECTIVE: This study evaluates MORL-ECSO under an auditable, paired-seed simulation protocol and compares it with tuned...
The results indicate that task-aware objective adaptation and chip-level control can reduce wasteful offloading and thermal stress while preserving service timeliness.
HRL-TaskOpt is presented, a novel Hierarchical Reinforcement Learning-based task scheduling framework that combines high-level global task offloading with millisecond-granularity local scheduling policies in an edge–cloud scenario and offers a scalable and intelligent solution for adaptive task scheduling.
The ER-RL-DVFS framework combines an energy-risk task-priority score, DVFS-aware cluster–PM–VM profiling, hierarchical candidate filtering, deterministic score-based assignment for clearly separated candidates, selective tabular reinforcement learning as the prescribed refinement mechanism for near-tie decisions, and m...
Experimental results demonstrate that the proposed method consistently outperforms nine state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) in terms of convergence, feasibility, and diversity, thereby confirming its effectiveness and strong general applicability.
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