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모바일 엣지 컴퓨팅 환경에서의 개인화 서비스 추천

Personalized Service Recommendation for Mobile Edge Computing Environment

  • Yim, Jong-choul (Electronics and Telecommunications Research Institute) ;
  • Kim, Sang-ha (Chungnam National Univ. Department of Computer Science & Engineering) ;
  • Keum, Chang-sup (Electronics and Telecommunications Research Institute)
  • 투고 : 2016.12.08
  • 심사 : 2017.05.17
  • 발행 : 2017.05.31

초록

모바일 엣지 컴퓨팅은 폭증하는 모바일 트래픽에 대응하고 다양한 요구사항을 만족시키는 서비스를 제공하기 위해 모바일 엣지 노드에서 다양한 기능을 직접 제공하는 기술이다. 예를 들어 모바일 트래픽 경감을 위한 캐싱이나, 위험감지 서비스 제공을 위한 비디오 분석 등이 모바일 엣지 노드에서 수행될 수 있다. 지금까지 개인화된 서비스를 추천하는 방법이나 구조 등에 대한 많은 연구가 있었지만, 모바일 엣지 컴퓨팅의 특성을 고려한 연구는 없었다. 개인화된 서비스를 제공하기 위해서는 사용자의 컨텍스트 정보를 획득하는 것이 중요하다. 기존 서버단 중심의 개인화된 서비스 모델은 모바일 엣지 컴퓨팅에 적용될 경우 컨텍스트 고립 문제와 프라이버시 이슈를 더욱 심화시킬 수 있다. 모바일 엣지 노드는 컨텍스트 수집이 용이하다는 이점을 가진다. 모바일 엣지 컴퓨팅 환경에서의 또 하나의 주목할 만한 특징은 사용자와 어플리케이션의 상호 연동이 매우 유동적이라는 점이다. 본 논문에서는 모바일 엣지 컴퓨팅의 특징을 반영한 로컬 서비스 추천 플랫폼 구조를 제시하고 컨텍스트 고립 문제와 프라이버시 이슈를 완화할 수 있는 개인화된 서비스 제공 방법을 제시한다.

Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.

키워드

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