Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning

Abstract

5G and beyond (B5G) applications generate tremendous computing-intensive, latency-sensitive, and privacy-sensitive tasks, which differ from the legacy cloud computing tasks, requiring more sophisticated scheduling strategies. We must satisfy the stringent service requirements, particularly privacy preservation that has not been sufficiently considered in the past. Meanwhile, we need to balance the tasks offloaded to different edge nodes to avoid overwhelming some fog nodes, which may degrade the overall performance. To appropriately schedule the privacy-sensitive tasks while balancing the traffic load, we define IoT tasks according to their security need, processing time, and real-time requirement and segment IoT tasks into smaller pieces based on their privacy levels. The sliced tasks are scheduled to multiple fog nodes with satisfactory security reputations to avoid a compromised fog node handling a whole task. Meanwhile, we consider the constraint of the response time of all available fog nodes before scheduling IoT tasks to avoid chaos task scheduling that may overwhelm some fog nodes. Regarding this, we propose a reinforcement learning (RL) model in which the agent tends to satisfy the required latency and security requirements while avoiding overloading some fog nodes to minimize the average delay. The numerical results demonstrate that the proposed approach performs well in a better-balanced load and less performance violation in latency and security.