한국게임학회 논문지 (Journal of Korea Game Society)

  • 제17권5호
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  • Pages.103-112
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  • 2017
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  • 1598-4540(pISSN)
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  • 2287-8211(eISSN)
한국게임학회 (Korea Game Society)
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Sparse Autoencoder의 데이터 특징 추출과 ProGReGA-KF를 결합한 새로운 부하 분산 알고리즘

Combing data representation by Sparse Autoencoder and the well-known load balancing algorithm, ProGReGA-KF

  • 투고 : 2017.09.11
  • 심사 : 2017.10.20
  • 발행 : 2017.10.20
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초록

많은 사용자가 함께 즐기는 온라인 게임(MMOGs)에서 IoT의 확장은 서버에 엄청난 부하를 지속적으로 증가시켜, 모든 데이터들이 Big-Data화 되어가는 환경에 있다. 이에 본 논문에서는 딥러닝 기법 중에서 가장 많이 사용되는 Sparse Autoencoder와 이미 잘 알려진 부하분산 알고리즘(ProGReGA-KF)을 결합한다. 기존 알고리즘 ProGReGA-KF과 본 논문에서 제안한 알고리즘을 이동 안정성으로 비교하였고, 제안한 알고리즘이 빅-데이터 환경에서 좀 더 안정적이고 확장성이 있음 시뮬레이션을 통해 보였다.

In recent years, expansions and advances of the Internet of Things (IoTs) in a distributed MMOGs (massively multiplayer online games) architecture have resulted in massive growth of data in terms of server workloads. We propose a combing Sparse Autoencoder and one of platforms in MMOGs, ProGReGA. In the process of Sparse Autoencoder, data representation with respect to enhancing the feature is excluded from this set of data. In the process of load balance, the graceful degradation of ProGReGA can exploit the most relevant and less redundant feature of the data representation. We find out that the proposed algorithm have become more stable.

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참고문헌

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