QoS-Aware Task Offloading in Fog Environment Using Multi-agent Deep Reinforcement Learning

With the surge of intelligent devices, applications of Internet of Things (IoT) are growing at a rapid pace. As a result, a massive amount of raw data is generated, which must be processed and stored. IoT devices standalone are not enough to handle large amount of data. Hence, to improve the performance, users started to push some jobs to far-situated cloud data centers, which would lead to more complications such as high bandwidth usage, service latency, and energy consumption. Fog computing emerges as a key enabling technology that brings cloud services closer to the end-user. However, owing to the unpredictability of tasks and Quality of Service (QoS) requirements of users, efficient task scheduling and resource allocation mechanisms are needed to balance the demand. To handle the problem efficiently, we have designed the task offloading problem as Markov Decision Process (MDP) by considering various user QoS factors including end-to-end latency, energy consumption, task deadline, and priority. Three different model-free off-policy Deep Reinforcement Learning (DRL) based solutions are outlined to maximize the reward in terms of resource utilization. Finally, extensive experimentation is conducted to validate and compare the efficiency and effectiveness of proposed mechanisms. Results show that with the proposed method, on average 96.23% of tasks can satisfy the deadline with an 8.25% increase.