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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Abstract	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.
AbstractList	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.
Author	Zhou, Zhangbing Meng, Lin Zhang, Yaqiang Zhang, Zhenjiang Shi, Zhensheng
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Cites_doi	10.1109/TNET.2019.2907243 10.1109/COMST.2017.2745201 10.1016/j.future.2019.05.070 10.1109/WCNC.2018.8377343 10.1109/TNN.1998.712192 10.1109/TMC.2019.2928811 10.1109/JIOT.2016.2565516 10.1109/ICC.2019.8761143 10.1109/ACCESS.2019.2924032 10.1016/j.jpdc.2019.02.003 10.1109/ACCESS.2019.2902846 10.1109/COMST.2018.2849509 10.1109/TVT.2018.2868013 10.1109/TPDS.2019.2961905 10.1109/TII.2018.2846549 10.1109/TMC.2018.2831230 10.1109/PERCOMW.2018.8480262 10.1109/MobileCloud.2017.11 10.1109/MCOM.2017.1600863 10.1109/INFOCOM.2019.8737409 10.1109/INFCOMW.2014.6849257 10.1109/TII.2018.2872579 10.1109/MCOM.001.1900094 10.1109/TNET.2018.2880874 10.1109/TSC.2016.2638083 10.1109/TII.2018.2843169 10.1109/WiMOB.2015.7348012 10.1109/TII.2019.2925023 10.1109/JIOT.2019.2900550 10.1109/JIOT.2016.2579198 10.1109/TCC.2015.2511727 10.1109/JIOT.2018.2876279 10.1109/JIOT.2019.2933587 10.1109/TNET.2015.2487344 10.1109/TMC.2019.2891736
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SubjectTerms	[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
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Title	Online Scheduling Optimization for DAG-Based Requests Through Reinforcement Learning in Collaboration Edge Networks
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