This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
Workflow scheduling in heterogeneous cloud environments must operate under dynamic and heterogeneous conditions. Heuristic algorithms cannot adapt online to changing conditions, while single-agent deep reinforcement learning (DRL) faces issues such as exponential growth in the action space, excessive training time, and limited interpretability. This paper proposes MAR-XGB, a hybrid scheduling framework that combines multi-agent reinforcement learning (MARL) with an XGBoost predictor. The tree ensemble estimates task runtime and execution cost, from which an urgency score is derived. Each agent uses lightweight Monte Carlo Tree Search (MCTS) to select VM locations and make online placement decisions over the Top-k tasks, ensuring real-time adaptability while reducing action-space complexity and improving prediction transparency and decision auditability. Experiments on representative WfCommons scientific workflows and controlled high-load DAG scenarios show that our approach outperforms traditional and state-of-the-art schedulers in terms of makespan, resource utilization, and cost efficiency. As the complexity and scale of workflows increase, our approach demonstrates improved scalability, indicating its potential to handle dynamic cloud workloads with fluctuating demand.
Fan Ding, Shuzhan Zhang, Yaqian Yuan· IEEE Transactions on Service...· 0 citations
Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.
Bekzat Kobei, N. Seilova, Zarina A. Kashaganova· AI@DTESI· 0 citations
Enabling collaborative generative AI (GenAI) inference at the network edge is challenging due to limited caching capacity, heterogeneous computing resources, and highly dynamic, latency-sensitive service demands. In this paper, we investigate the joint optimization of GenAI model caching, inference offloading, and resource allocation in a collaborative cloud–edge–end architecture. To address the strong coupling between long-term caching decisions and short-term scheduling dynamics, we propose a Hierarchical Meta-Graph Reinforcement Learning framework, termed HMGRL. Specifically, a heat-greedy model caching strategy is developed to capture time-varying model popularity and to reduce switching overhead on a slow timescale, while a graph-enhanced dueling deep reinforcement learning algorithm with prioritized experience replay enables topology-aware collaborative inference offloading and resource allocation on a fast timescale. Extensive simulations demonstrate that HMGRL consistently outperforms representative baselines in terms of system utility, cache and computing-resource utilization, convergence stability, and performance robustness. These results validate the effectiveness of the proposed hierarchical learning framework for practical GenAI applications at the network edge.
Liang Zhao, Jing Wei, Huan Zhou et al.· IEEE Transactions on Cogniti...· 0 citations
A two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power and achieves near-optimal performance compared to branch-and-bound solutions.
V. Son, Van-Dinh Nguyen, Ngoc Hung Nguyen et al.· 0 citations
SAGE (Scene-Adaptive Graph-Enhanced offloading), a task-offloading framework that combines a heterogeneous graph neural network (HeteroGNN) with a dueling double DQN meta-controller and a mixed-integer linear programming (MILP) solver, is proposed, demonstrating strong scene-adaptive decision-making capability.
Lingtao Xue, Xuewen Dong, Xinyu Hu et al.· Electronics· 0 citations
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