Joint Task Scheduling and Resource Allocation in Cloud-Edge Collaborative Computing Systems

Cloud-edge collaborative computing (CECC) facilitates the sharing of computing resources by collaboratively scheduling tasks among servers, thereby maximizing task execution efficiency. Task scheduling and resource allocation (TS-RA) are two interrelated issues that significantly affect the efficient utilization of computing resources. In this paper, we decouple the joint optimization problem of TS-RA and propose a novel model based on multi-agent reinforcement learning (TRMARL), which is applicable to distributed task scheduling and resource allocation in a heterogeneous CECC system. TRMARL consists of two modules: 1) the task scheduling module, where we introduce a value factorization algorithm to maximize joint rewards of distributed scheduling actions; 2) the resource allocation module, where we present a proximal policy optimization (PPO) algorithm based mechanism to optimize resource allocation. TRMARL efficiently captures the state difference among heterogeneous servers through a graph attention network-based recurrent deep Q-network (GAT-based recurrent-DQN) architecture and learns different strategies for heterogeneous services through a multi-expert schema. The experimental results demonstrate that TRMARL effectively improves the task completion rate, reduces average system latency, and enhances convergence stability in a heterogeneous CECC system.