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학위논문 상세정보

Active content collaboration framework with adaptive resource management platform on hybrid cloud 원문보기

  • 저자

    한병전

  • 학위수여기관

    Graduate School, Korea University

  • 학위구분

    국내박사

  • 학과

    電氣電子電波工學科

  • 지도교수

    丁昌聲

  • 발행년도

    2014

  • 총페이지

    ix, 103장

  • 키워드

    Cloud computing Grid computing content management system collaboration platform;

  • 언어

    eng

  • 원문 URL

    http://www.riss.kr/link?id=T13541308&outLink=K  

  • 초록

    In this paper, we shall present a content-centric collaboration platform called Active Content Collaboration Framework (ACCF) which supports automated content centric collaboration on Adaptive Resource Management Platform (ARMP). ACCF supports event-driven automatic collaboration by specifying each active work based on AWDL and automating the execution of collaborative task flow composed of active works. It supports a modularized and extensible architecture by making its components in separate modules. Also, it provides a scalable high performance architecture by supporting multi-level active work processing in AWEE, and allocating virtual machines for the computation intensive high-level action such as volume rendering through auto-scalable allocation on Cloud system. To support fast resource provisioning service and job execution service for ACCF, ARMP manages both Cloud resources and Grid resources. For fast resource provisioning service, it provides two kinds of resource request prediction module: pattern matching predictor and probabilistic predictor. By predicting future request, ARMP reduces virtual machine setup time. Pattern matching predictor analyzes continuous series of previous resource requests to predict the number of next resource in patterned job. Probabilistic predictor finds upper bound and lower bound of the number of resources to maintain suitable size of resource pool. Each predictor deals with different kinds of resource usage type. Also, ARMP supports fast job execution service by using Grid management system which manages several physical resources. When job execution is requested, ARMP finds proper Grid resource to execute requested job by using Matching Policy Analyzer (MPA).


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