한국시뮬레이션학회논문지 (Journal of the Korea Society for Simulation)

한국시뮬레이션학회 (The Korea Society for Simulation)
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클라우드 프로비저닝 서비스를 위한 퍼지 로직 기반의 자원 평가 방법

Fuzzy Logic-driven Virtual Machine Resource Evaluation Method for Cloud Provisioning Service

  • 김재권 (인하대학교 정보공학과) ;
  • 이종식 (인하대학교 정보공학과)
  • 투고 : 2012.07.03
  • 심사 : 2012.09.25
  • 발행 : 2013.03.31
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초록

클라우드 환경은 여러 개의 컴퓨팅 자원들을 이용하는 분산 컴퓨팅 환경의 일종으로 가상머신을 이용 하여 작업을 처리한다. 클라우드 환경은 작업 요청에 따르는 부하분산과 빠른 작업 처리를 위한 프로비저닝 기술을 이용하여 가상머신의 상태에 따라 작업을 할당 한다. 하지만, 클라우드 환경의 작업 스케줄링을 위해서는 가상머신의 성능에 따르는 애매모호한 상태에 대한 가용성의 정의가 필요하다. 본 논문에서는 클라우드 환경의 프로비저닝 스케줄링을 위해 퍼지 로직 기반의 자원평가를 이용한 가상머신 프로비저닝 스케줄링(FVPRE: Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation)을 제안한다. FVPRE는 각 가상머신의 정의하기 어려운 성능의 상태를 분석하여 자원 가용성에 대한 값을 구체화하여 정확한 자원의 가용성 평가를 통해 효율적인 프로비저닝 스케줄링이 가능하다. FVPRE는 클라우드 환경의 작업 처리에 대해 높은 처리율과 활용율을 보인다.

Cloud computing is one of the distributed computing environments and utilizes several computing resources. Cloud environment uses a virtual machine to process a requested job. To balance a workload and process a job rapidly, cloud environment uses a provisioning technique and assigns a task with a status of virtual machine. However, a scheduling method for cloud computing requires a definition of virtual machine availabilities, which have an obscure meaning. In this paper, we propose Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation(FVPRE). FVPRE analyzes a state of every virtual machine and actualizes a value of resource availability. Thus FVPRE provides an efficient provisioning scheduling with a precise evaluation of resource availability. FVPRE shows a high throughput and utilization for job processing on cloud environments.

키워드

참고문헌

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피인용 문헌

  1. Adaptive Resource Management Method base on ART in Cloud Computing Environment vol.23, pp.4, 2014, https://doi.org/10.9709/JKSS.2014.23.4.111
  2. Adaptive resource provisioning method using application-aware machine learning based on job history in heterogeneous infrastructures vol.20, pp.4, 2017, https://doi.org/10.1007/s10586-017-1148-1
  3. Hybrid cloud entropy systems based on Wiener process vol.45, pp.7, 2016, https://doi.org/10.1108/K-01-2016-0010
  4. Job Classifying method based on Data Traits for Increased Efficiency of Computational Resources in Distributed Environment vol.23, pp.4, 2014, https://doi.org/10.9709/JKSS.2014.23.4.219