Mayfly Taylor Optimization-Based Graph Attention Network for Task Scheduling in Edge Computing

Abstract

Multi-access edge computing (MEC) is a technology that enables devices with limited processing capabilities to handle computationally intensive tasks efficiently. The challenge with MEC is how to schedule multiple tasks rationally and efficiently, mainly when each device generates several tasks. A critical aspect of task scheduling is considering the spatial relationships between devices in the network to reduce long-term losses. To address these challenges, this article proposes a deep reinforcement learning (DRL)-based Mayfly Taylor optimization algorithm (MTOA) that uses graph attention neural networks (GATs). The algorithm creates a planning agent for each end device that collects timing-related characteristics of activities and makes decisions and predictions using a recurrent gated unit and a graph representation agent (GRU). The GRU contains possible spatial elements from the scenario and incorporates them into the decision-making process. A novel approach, the Mayfly Taylor optimization algorithm (MTOA), for addressing the challenges in Multi-access Edge Computing (MEC) task scheduling. The key innovation lies in the integration of deep reinforcement learning (DRL) with graph attention neural networks (GATs) to create an effective scheduling strategy. The proposed algorithm outperforms several baseline methods by exploiting the spatial positional relationships between devices. It significantly reduces average latency, bandwidth, and dropout rates and increases in link usage efficiency. Overall, the proposed approach provides a solution to the task-scheduling problem in the MEC scenario that considers separable and time-sensitive activities while benefiting from the spatial relationships between devices.