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Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing
IEEE Transactions on Cloud Computing ( IF 6.5 ) Pub Date : 2020-10-01 , DOI: 10.1109/tcc.2016.2594172
Keke Gai , Longfei Qiu , Hui Zhao , Meikang Qiu

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 optimizations 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.

中文翻译:

云计算中使用遗传算法的异构内存成本感知多媒体数据分配

最近应用云计算的物联网 (IoT) 扩展以惊人的速度增长。作为发展趋势之一,异构云计算使得多媒体大数据等多种基于云的基础设施解决方案成为可能。许多先前的研究已经探索了内部异构内存的优化。然而,由于硬件分布和操纵机制引起的性能限制和成本问题,异构云存储器正面临限制。将数据任务分配给具有各种容量的分布式存储器是一个组合 NP-hard 问题。本文重点关注这个问题,并提出了一种新方法,即成本感知异构云内存模型 (CAHCM),旨在提供基于云的高性能异构内存服务产品。支持 CAHCM 的主要算法是动态数据分配提前 (2DA) 算法,它使用遗传编程来确定基于云的内存上的数据分配。在我们提出的方法中,我们考虑了一组影响云存储器性能的关键因素,例如通信成本、数据移动运营成本、能源性能和时间限制。最后,我们实施实验评估来检查我们提出的模型。实验结果表明,我们的方法是可采用且可行的,可作为具有成本意识的基于云的解决方案。我们考虑了一组影响云存储器性能的关键因素,例如通信成本、数据移动运营成本、能源性能和时间限制。最后,我们实施实验评估来检查我们提出的模型。实验结果表明,我们的方法是可采用且可行的,可作为具有成本意识的基于云的解决方案。我们考虑了一组影响云存储器性能的关键因素,例如通信成本、数据移动运营成本、能源性能和时间限制。最后,我们实施实验评估来检查我们提出的模型。实验结果表明,我们的方法是可采用且可行的,可作为具有成本意识的基于云的解决方案。
更新日期:2020-10-01
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