Journal of Information Processing Systems

  • 제17권5호
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  • Pages.905-917
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  • 2021
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Range Segmentation of Dynamic Offloading (RSDO) Algorithm by Correlation for Edge Computing

  • Kang, Jieun (Dept. of IT Engineering, Sookmyung Women's University) ;
  • Kim, Svetlana (Dept. of IT Engineering, Sookmyung Women's University) ;
  • Kim, Jae-Ho (Dept. of Electronics and Information Engineering, Sejong University) ;
  • Sung, Nak-Myoung (Korea Electronics Technology Institute (KETI)) ;
  • Yoon, Yong-Ik (Dept. of IT Engineering, Sookmyung Women's University)
  • 투고 : 2021.01.21
  • 심사 : 2021.06.27
  • 발행 : 2021.10.31
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초록

In recent years, edge computing technology consists of several Internet of Things (IoT) devices with embedded sensors that have improved significantly for monitoring, detection, and management in an environment where big data is commercialized. The main focus of edge computing is data optimization or task offloading due to data and task-intensive application development. However, existing offloading approaches do not consider correlations and associations between data and tasks involving edge computing. The extent of collaborative offloading segmented without considering the interaction between data and task can lead to data loss and delays when moving from edge to edge. This article proposes a range segmentation of dynamic offloading (RSDO) algorithm that isolates the offload range and collaborative edge node around the edge node function to address the offloading issue.The RSDO algorithm groups highly correlated data and tasks according to the cause of the overload and dynamically distributes offloading ranges according to the state of cooperating nodes. The segmentation improves the overall performance of edge nodes, balances edge computing, and solves data loss and average latency.

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과제정보

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent technology for adaptive and disposable IoT). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2019R1I1A1A01064054).

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