A Graph Attention Mechanism-Based Multiagent Reinforcement-Learning Method for Task Scheduling in Edge Computing

Abstract

Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization.

A Multi-Edge Jointly Offloading Method Considering Group Cooperation Topology Features in Edge Computing Networks

“…Experimental results show that the introduction of GAT can enhance temporal and spatial cooperation between agents. Reference [26] used GAT to extract objectively existing spatial structural relationships in an established scenario and used multi-agent reinforcement learning to obtain a scheduling agent for each server to extract temporal correlation features of tasks and make decisions. The results show that this method can significantly reduce average latency and packet loss rate and improve link utilization.…”

Batch-Wise Permutation Feature Importance Evaluation and Problem-Specific Bigraph for Learn-to-Branch

“…In addition, using specialized deep learning and parallel computing hardware for ML models, ML-B&B can be much faster than traditional B&B implementations. Generally speaking, the training of ML models for B&B follows one of two methodologies: imitation learning (IL) and reinforcement learning (RL) [20]. In IL, the ML model is trained through the demonstration of an expert solver, such as the default MILP solver of SCIP [5].…”