Intelligent Task Scheduling in Edge-Cloud Environments Using Double Deep Q-Network Reinforcement Learning
TL;DR
Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency.
Abstract
In today’s world, with the emergence of IoT devices and critical latency applications, there has been an increased demand for intelligent resource management in diverse computing environments [5][10]. Collaborative computing is the integration of cloud and edge computing, while the issue of how to schedule each task for execution is still an open problem. Traditional heuristics like Round Robin and greedy latency reduction are incapable of adapting to the stochastic and non-stationary characteristics of practical workloads [4][14]. In this paper, an intelligent task scheduling mechanism based on Double Deep Q-Network (DDQN) reinforcement learning [2] has been proposed. The agent observes a four-dimensional state encoding task characteristics and selects binary offloading decisions, guided by a shaped reward signal encoding multiple performance objectives. Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency. These results confirm that deep reinforcement learning [1][17][18] provides a principled foundation for adaptive resource management in next-generation edge-cloud systems.