The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Traffic Offloading Algorithm Using Social Context in MEC Environment

MEC 환경에서의 Social Context를 이용한 트래픽 오프로딩 알고리즘

  • Cheon, Hye-Rim (Ajou University Department of Elecrical and Computer Engineering) ;
  • Lee, Seung-Que (Electronics and Telecommunications Research Institute) ;
  • Kim, Jae-Hyun (Ajou University Department of Elecrical and Computer Engineering)
  • Received : 2016.12.02
  • Accepted : 2017.01.10
  • Published : 2017.02.28
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Abstract

Traffic offloading is a promising solution to solve the explosive growth of mobile traffic. One of offloading schemes, in LIPA/SIPTO(Local IP Access and Selected IP Traffic Offload) offloading, we can offload mobile traffic that can satisfy QoS requirement for application. In addition, it is necessary for traffic offloading using social context due to large traffic from SNS. Thus, we propose the LIPA/SIPTO offloading algorithm using social context. We define the application selection probability using social context, the application popularity. Then, we find the optimal offloading weighting factor to maximize the QoS(Quality of Service) of small cell users in term of effective data rate. Finally, we determine the offloading ratio by this application selection probability and optimal offloading weighting factor. By performance analysis, the effective data rate achievement ratio of the proposed algorithm is similar with the conventional one although the total offloading ratio of the proposed algorithm is about 46 percent of the conventional one.

트래픽 오프로딩은 폭발적으로 증가하는 모바일 트래픽에 대응하기 위한 유망 솔루션이다. 오프로딩 방법 중, LIPA/SIPTO 오프로딩에서는 애플리케이션의 QoS 요구사항을 만족하면서 트래픽을 오프로딩할 수 있다. 또한, SNS로 인한 많은 트래픽때문에 social context를 이용한 트래픽 오프로딩이 필요하다. 그러므로, 본 논문에서는 social context를 이용하여 트래픽을 오프로딩하는 LIPA/SIPTO 오프로딩 알고리즘을 제안한다. 먼저, 애플리케이션 인기도를 social context로 이용하여 애플리케이션 선택확률을 정의한다. 그 다음, effective data rate 관점에서 소형셀 사용자의 QoS를 최대화하는 최적의 오프로딩 weighting factor를 찾는다. 마지막으로, 애플리케이션 선택확률과 오프로딩 weighting factor를 기반으로 각 애플리케이션의 오프로딩 비율을 정한다. 성능분석 결과, 제안한 알고리즘의 오프로딩 비율이 기존 알고리즘의 약 46%임에도 불구하고, 제안한 알고리즘의 effective data rate achievement ratio 값이 기존 알고리즘과 비슷한 것을 확인하였다.

Keywords

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