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Volume 12
Issue 14
10.3390/app12147137
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Article

Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning

by
Pablo Sanabria
1,2,
Tomás Felipe Tapia
1,
Rodrigo Toro Icarte
1,2 and
Andres Neyem
1,2,*
1
Computer Science Department, Pontificia Universidad Catolica de Chile, Santiago 7820436, Chile
2
Centro Nacional de Inteligencia Artificial CENIA, Santiago 7820436, Chile
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(14), 7137; https://doi.org/10.3390/app12147137
Submission received: 26 June 2022 / Revised: 9 July 2022 / Accepted: 11 July 2022 / Published: 15 July 2022
(This article belongs to the Special Issue Distributed Computing Systems and Applications)
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Abstract

Due to mobile and IoT devices’ ubiquity and their ever-growing processing potential, Dew computing environments have been emerging topics for researchers. These environments allow resource-constrained devices to contribute computing power to others in a local network. One major challenge in these environments is task scheduling: that is, how to distribute jobs across devices available in the network. In this paper, we propose to distribute jobs in Dew environments using artificial intelligence (AI). Specifically, we show that an AI agent, known as Proximal Policy Optimization (PPO), can learn to distribute jobs in a simulated Dew environment better than existing methods—even when tested over job sequences that are five times longer than the sequences used during the training. We found that using our technique, we can gain up to 77% in performance compared with using human-designed heuristics.
Keywords: Dew computing; reinforcement learning; scheduling algorithms Dew computing; reinforcement learning; scheduling algorithms

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MDPI and ACS Style

Sanabria, P.; Tapia, T.F.; Toro Icarte, R.; Neyem, A. Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning. Appl. Sci. 2022, 12, 7137. https://doi.org/10.3390/app12147137

AMA Style

Sanabria P, Tapia TF, Toro Icarte R, Neyem A. Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning. Applied Sciences. 2022; 12(14):7137. https://doi.org/10.3390/app12147137

Chicago/Turabian Style

Sanabria, Pablo, Tomás Felipe Tapia, Rodrigo Toro Icarte, and Andres Neyem. 2022. "Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning" Applied Sciences 12, no. 14: 7137. https://doi.org/10.3390/app12147137

APA Style

Sanabria, P., Tapia, T. F., Toro Icarte, R., & Neyem, A. (2022). Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning. Applied Sciences, 12(14), 7137. https://doi.org/10.3390/app12147137

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