Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning

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Gazori Pegah; Rahbari Dadmehr; Nickray Mohsen

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Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2020 | Volume:18 | Issue:2 Page(s): 127-137

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Title

Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning

Author(s)

Gazori Pegah | Rahbari Dadmehr | Nickray Mohsen | Issue Writer Certificate

Keywords

Internet of ThingsQ1

Fog computingQ1

Task SchedulingQ1

Deep reinforcement learningQ1

Abstract

With the advent and development of IoT applications in recent years, the number of smart devices and consequently the volume of data collected by them are rapidly increasing. On the other hand, most of the IoT applications require real-time data analysis and low latency in service delivery. Under these circumstances, sending the huge volume of various data to the cloud data centers for processing and analytical purposes is impractical and the Fog computing paradigm seems a better choice. Because of limited computational resources in fog nodes, efficient utilization of them is of great importance. In this paper, the scheduling of IoT application tasks in the Fog computing paradigm has been considered. The main goal of this study is to reduce the latency of service delivery, in which we have used the Deep reinforcement learning approach to meet it. The proposed method of this paper is a combination of the Q-Learning algorithm, deep learning, experience replay, and target network techniques. According to experiment results, The DQLTS algorithm has improved the ASD metric by 76% in comparison to QLTS and 6. 5% compared to the RS algorithm. Moreover, it has been reached to faster convergence time than QLTS.

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APA: Copy

Gazori, Pegah, Rahbari, Dadmehr, & Nickray, Mohsen. (2020). Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 18(2 ), 127-137. SID. https://sid.ir/paper/403581/en

Vancouver: Copy

Gazori Pegah, Rahbari Dadmehr, Nickray Mohsen. Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2020;18(2 ):127-137. Available from: https://sid.ir/paper/403581/en

IEEE: Copy

Pegah Gazori, Dadmehr Rahbari, and Mohsen Nickray, “Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 18, no. 2 , pp. 127–137, 2020, [Online]. Available: https://sid.ir/paper/403581/en

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