Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system

Zeng, Deze, Gu, Lin, Guo, Song, Cheng, Zixue and Yu, Shui 2016, Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system, IEEE transactions on computers, vol. 65, no. 12, pp. 3702-3712, doi: 10.1109/TC.2016.2536019.

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Title Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system
Author(s) Zeng, Deze
Gu, Lin
Guo, Song
Cheng, Zixue
Yu, ShuiORCID iD for Yu, Shui
Journal name IEEE transactions on computers
Volume number 65
Issue number 12
Start page 3702
End page 3712
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-12-01
ISSN 0018-9340
Keyword(s) fog computing
software-defined embedded system
task scheduling
resource management
Science & Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Summary Traditional standalone embedded system is limited in their functionality, flexibility, and scalability. Fog computing platform, characterized by pushing the cloud services to the network edge, is a promising solution to support and strengthen traditional embedded system. Resource management is always a critical issue to the system performance. In this paper, we consider a fog computing supported software-defined embedded system, where task images lay in the storage server while computations can be conducted on either embedded device or a computation server. It is significant to design an efficient task scheduling and resource management strategy with minimized task completion time for promoting the user experience. To this end, three issues are investigated in this paper: 1) how to balance the workload on a client device and computation servers, i.e., task scheduling, 2) how to place task images on storage servers, i.e., resource management, and 3) how to balance the I/O interrupt requests among the storage servers. They are jointly considered and formulated as a mixed-integer nonlinear programming problem. To deal with its high computation complexity, a computation-efficient solution is proposed based on our formulation and validated by extensive simulation based studies.
Language eng
DOI 10.1109/TC.2016.2536019
Field of Research 0803 Computer Software
0805 Distributed Computing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, iEEE
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