Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing
| Autor: | Yuxuan Yao, Lenan Wu, Peng Chen, Zhimin Chen, Shuran Sheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
task scheduling
Edge device Computer science Distributed computing markov decision process (MDP) 02 engineering and technology computer.software_genre lcsh:Chemical technology Biochemistry Article Analytical Chemistry Scheduling (computing) Task (project management) 0203 mechanical engineering edge computing 0202 electrical engineering electronic engineering information engineering Reinforcement learning lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Edge computing deep reinforcement learning (DRL) Job shop scheduling 020302 automobile design & engineering 020206 networking & telecommunications Atomic and Molecular Physics and Optics Internet of Things (IoT) Virtual machine Resource allocation Markov decision process computer |
| Zdroj: | Sensors, Vol 21, Iss 1666, p 1666 (2021) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 5 |
| ISSN: | 1424-8220 |
| Popis: | Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio. |
| Databáze: | OpenAIRE |
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