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Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
صرفه جویی در وقت و هزینه در برنامه ریزی کاربردهای اینترنت اشیا-مبتنی بر مه با استفاده از روش یادگیری تقویتی عمیق-2020 Due to the rapid growth of intelligent devices and the Internet of Things (IoT) applications in recent
years, the volume of data that is generated by these devices is increasing ceaselessly. Hence, moving
all of these data to cloud datacenters would be impossible and would lead to more bandwidth usage,
latency, cost, and energy consumption. In such cases, the fog layer would be the best place for data
processing. In the fog layer, the computing equipment dedicates parts of its limited resources to process
the IoT application tasks. Therefore, efficient utilization of computing resources is of great importance
and requires an optimal and intelligent strategy for task scheduling. In this paper, we have focused
on the task scheduling of fog-based IoT applications with the aim of minimizing long-term service
delay and computation cost under the resource and deadline constraints. To address this problem,
we have used the reinforcement learning approach and have proposed a Double Deep Q-Learning
(DDQL)-based scheduling algorithm using the target network and experience replay techniques. The
evaluation results reveal that our proposed algorithm outperforms some baseline algorithms in terms
of service delay, computation cost, energy consumption and task accomplishment and also handles
the Single Point of Failure (SPoF) and load balancing challenges. Keywords: Fog computing | Task scheduling | Deep reinforcement learning | Double Q-Learning | Service delay | Computation cost |
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