Neighborhood search-based job scheduling for IoT big data real-time processing in distributed edge-cloud computing environment

Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification,...

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Published in:	The Journal of supercomputing Vol. 77; no. 2; pp. 1853 - 1878
Main Authors:	Li, Chunlin, Zhang, YiHan, Luo, Youlong
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2021 Springer Nature B.V
Subjects:	Algorithms Caching Cloud computing Clustering Compilers Computer Science Data centers Interpreters Job classification Neighborhoods Nodes Placement Processor Architectures Programming Languages Resource allocation Scheduling Searching Streaming media Neighborhood search Distributed edge-cloud Job scheduling
ISSN:	0920-8542, 1573-0484
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Abstract	Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cache-aware job scheduling. Firstly, jobs are categorized into three categories, and then different resources are allocated to nodes according to different job execution conditions. Secondly, the nodes with similar capabilities are clustered, and the jobs are cached by delay-waiting. For jobs that do not satisfy the data locality, the jobs are scheduled to the nodes with similar capabilities according to the neighborhood search results. Meanwhile, a cache-aware scheduling algorithm based on neighborhood search is proposed. Experiments show that the proposed algorithm can effectively minimize the delay of content transmission and the cost of content placement, the job execution time is shortened and the processing capacity of the cloud data center is improved.
AbstractList	Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cache-aware job scheduling. Firstly, jobs are categorized into three categories, and then different resources are allocated to nodes according to different job execution conditions. Secondly, the nodes with similar capabilities are clustered, and the jobs are cached by delay-waiting. For jobs that do not satisfy the data locality, the jobs are scheduled to the nodes with similar capabilities according to the neighborhood search results. Meanwhile, a cache-aware scheduling algorithm based on neighborhood search is proposed. Experiments show that the proposed algorithm can effectively minimize the delay of content transmission and the cost of content placement, the job execution time is shortened and the processing capacity of the cloud data center is improved.
Author	Zhang, YiHan Li, Chunlin Luo, Youlong
Author_xml	– sequence: 1 givenname: Chunlin surname: Li fullname: Li, Chunlin organization: Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Department of Computer Science, Wuhan University of Technology – sequence: 2 givenname: YiHan surname: Zhang fullname: Zhang, YiHan organization: Department of Computer Science, Wuhan University of Technology – sequence: 3 givenname: Youlong surname: Luo fullname: Luo, Youlong email: luoyoulong2005@126.com organization: Department of Computer Science, Wuhan University of Technology
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Cites_doi	10.1002/jgt.3190130610 10.1145/1218063.1217968 10.1109/TSC.2018.2866421 10.1109/SNSP.2018.00096 10.1007/978-981-10-0129-1_4 10.1142/S0129626415500097 10.1109/OJCOMS.2020.2978585 10.1109/TSC.2015.2428251 10.1109/MNET.001.1800486 10.1109/TCC.2015.2474403 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00153 10.1109/ICNP.2019.8888037 10.1109/CAC48633.2019.8996876 10.1016/j.jss.2016.07.006 10.1049/iet-ipr.2017.0892 10.1007/s10586-016-0677-3 10.1016/j.future.2019.05.026 10.1109/TASE.2017.2693688
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SubjectTerms	Algorithms Caching Cloud computing Clustering Compilers Computer Science Data centers Interpreters Job classification Neighborhoods Nodes Placement Processor Architectures Programming Languages Resource allocation Scheduling Searching Streaming media
Title	Neighborhood search-based job scheduling for IoT big data real-time processing in distributed edge-cloud computing environment
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