Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment

Gongzhuang Peng, Hongwei Wang, Heming Zhang, Jietao Dong

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    Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic algorithm is proposed based on the elitist archive and the K-means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants’ computational requirements in a distributed environment.
    Original languageEnglish
    Pages (from-to)306-317
    Number of pages12
    JournalIEEE Transactions on Services Computing
    Issue number2
    Early online date14 Jan 2016
    Publication statusPublished - 1 Mar 2018


    • service oriented computing
    • Cloud Computing
    • collaborative simulation
    • multi tenancy agreement


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