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Priority-Based and Optimized Data Center Selection in Cloud Computing

机译：云计算中基于优先级和优化的数据中心选择

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摘要

Cloud computing (CC) is rapidly emerging in IT fields and becomes a vital research area. CC environment allows cloud service providers to offer different types of services to customers of different categories. Customers like their requests to be processed at the lowest cost and minimum response time, which is highly dependent on the receiving datacenter (DC). Selecting a DC is entitled to the service broker that operates in accordance with a built-in routing policy. Improper selection of a DC may cause a bottleneck in the service brokerage architecture, resulting in resource bleeding (time and money). Moreover, cloud services are charged based on a pay-per-use model. Therefore, this paper proposes an efficient service broker routing policy that improves users' satisfaction and cloud performance. The proposed policy employs the Vector Space Model and a multi-objective scalarization function to optimize conflicting objectives. The simulation-based approach was used to test the performance of the proposed methodology. The results show the ability of the proposed methodology to improve DC selection process based on the user's priorities. Moreover, the performance of the proposed methodology has been compared with related works; the results show a significant improvement in average DC processing time, response time and total cost.
机译：云计算（CC）在IT领域迅速兴起，并成为至关重要的研究领域。 CC环境允许云服务提供商向不同类别的客户提供不同类型的服务。客户希望以最低的成本和最短的响应时间来处理他们的请求，这在很大程度上取决于接收数据中心（DC）。选择DC有权获得根据内置路由策略运行的服务代理。 DC的选择不当可能会导致服务代理体系结构出现瓶颈，从而导致资源浪费（时间和金钱）。此外，云服务是基于按使用付费模式收费的。因此，本文提出了一种有效的服务代理路由策略，可提高用户满意度和云性能。提出的策略采用向量空间模型和多目标标量函数来优化冲突目标。基于模拟的方法用于测试所提出方法的性能。结果表明，所提出的方法具有改善基于用户优先级的DC选择过程的能力。此外，已将拟议方法的性能与相关工作进行了比较；结果表明，平均DC处理时间，响应时间和总成本得到了显着改善。

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