Online Scheduling Optimization for DAG-Based Requests Through Reinforcement Learning in Collaboration Edge Networks

The wide-adoption of edge computing promotes the scheduling of tasks in complex requests upon smart devices on the network edge, whereas tasks are necessary to be offloaded to the cloud when they are intensive in computational and energy resources. Traditional techniques explore mostly the schedulin...

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Vydané v:IEEE Access Ročník 8; s. 72985 - 72996
Hlavní autori: Zhang, Yaqiang, Zhou, Zhangbing, Shi, Zhensheng, Meng, Lin, Zhang, Zhenjiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2020
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
[SCCO.COMP]Cognitive science/Computer science
Cloud computing
Cognitive science
Collaboration
Computer science
Edge computing
Electrical engineering. Electronics. Nuclear engineering
Electronic devices
Energy consumption
Energy sources
Internet of Things
Learning
Markov processes
Mobile handsets
Online DAG-based request optimization
Optimization
Processor scheduling
Quality of experience
Scheduling
Servers
Task analysis
Task complexity
Task scheduling
temporal-difference learning
TK1-9971
Temporal-difference learning
Online DAG-based request optimization
Internet of Things
Edge computing
ISSN:2169-3536, 2169-3536
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Shrnutí:The wide-adoption of edge computing promotes the scheduling of tasks in complex requests upon smart devices on the network edge, whereas tasks are necessary to be offloaded to the cloud when they are intensive in computational and energy resources. Traditional techniques explore mostly the scheduling of atomic tasks, whereas complex requests scheduling on edge servers is the challenge unexplored extensively. To address this challenge, this paper proposes an online task scheduling optimization for DAG-based requests at the network edge, where this scheduling procedure is modeled as Markov decision process, in which system state, request and decision space are formally specified. A temporal-difference learning based mechanism is adopted to learn an optimal tasks allocation strategy at each decision stage. Extensive experiments are conducted, and evaluation results demonstrate that our technique can effectively reduce the system's long-term average delay and energy consumption in comparison with the state-of-art's counterparts.
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2987574