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Extracting optimal moving patterns of edge devices for efficient resource placement in an FEC environment

FEC 환경에서 효율적 자원 배치를 위한 엣지 디바이스의 최적 이동패턴 추출

  • Lee, YonSik (School of Computer Info. & Comm., Kunsan National University) ;
  • Nam, KwangWoo (School of Computer Info. & Comm., Kunsan National University) ;
  • Jang, MinSeok (School of Computer Info. & Comm., Kunsan National University)
  • Received : 2021.11.02
  • Accepted : 2021.11.18
  • Published : 2022.01.31

Abstract

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

동적으로 변하는 시간 가변적 네트워크 환경에서 엣지 디바이스의 최적 이동패턴은 FEC환경에서 응용 서비스 사용자에 근접한 에지 클라우드 서버에 컴퓨팅 리소스를 분배하거나 새로운 에지 서버(기지국)를 배치하는데 적용함으로써, 클라우드 컴퓨팅의 단점인 지연시간 문제 완화를 위한 효율적 계산 오프로딩이 가능한 환경 구축에 활용이 가능하다. 본 논문은 임의의 시간제약 및 이동규칙 등이 적용되는 시공간 환경에서 응용 서비스를 요구하는 다수의 엣지 디바이스(이동객체)들의 이동경로를 빈발도 기반으로 분석하여 최적 이동패턴을 추출하는 알고리즘을 제안한다. 제안한 OPE_freq 알고리즘을 A* 및 Dijkstra 알고리즘들과 비교 실험을 통하여, 제안 알고리즘이 상대적으로 빠른 연산시간과 적은 메모리를 사용하고 보다 정확한 최적경로를 추출함을 알 수 있다. 또한 A* 알고리즘과의 비교 결과를 통하여 가중치를 빈발도와 동시에 적용함으로써 경로 추출의 정확도를 향상시킬 수 있음을 도출하였다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1047768) and a grant (21RITD-C161698-01) from Regional Innovation Technology Development Program funded by Ministry of Land, Infrastructure and Transport of Korean government

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