Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing

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@Article{Gai:2017:ieeeCLOUD,
  author =       "Keke Gai and Meikang Qiu and Hui Zhao",
  journal =      "IEEE Transactions on Cloud Computing",
  title =        "Cost-Aware Multimedia Data Allocation for
                 Heterogeneous Memory Using Genetic Algorithm in Cloud
                 Computing",
  abstract =     "Recent expansions of Internet-of-Things (IoT) applying
                 cloud computing have been growing at a phenomenal rate.
                 As one of the developments, heterogeneous cloud
                 computing has enabled a variety of cloud-based
                 infrastructure solutions, such as multimedia big data.
                 Numerous prior researches have explored the
                 optimisations of on-premise heterogeneous memories.
                 However, the heterogeneous cloud memories are facing
                 constraints due to the performance limitations and cost
                 concerns caused by the hardware distributions and
                 manipulative mechanisms. Assigning data tasks to
                 distributed memories with various capacities is a
                 combinatorial NP-hard problem. This paper focuses on
                 this issue and proposes a novel approach, Cost-Aware
                 Heterogeneous Cloud Memory Model (CAHCM), aiming to
                 provision a high performance cloud-based heterogeneous
                 memory service offerings. The main algorithm supporting
                 CAHCM is Dynamic Data Allocation Advance (2DA)
                 Algorithm that uses genetic programming to determine
                 the data allocations on the cloud-based memories. In
                 our proposed approach, we consider a set of crucial
                 factors impacting the performance of the cloud
                 memories, such as communication costs, data move
                 operating costs, energy performance, and time
                 constraints. Finally, we implement experimental
                 evaluations to examine our proposed model. The
                 experimental results have shown that our approach is
                 adoptable and feasible for being a cost-aware
                 cloud-based solution.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TCC.2016.2594172",
  ISSN =         "2168-7161",
  notes =        "Also known as \cite{7523230}",
}

Genetic Programming entries for Keke Gai Meikang Qiu Hui Zhao

Citations