论文标题
通过Cloudlets的IoT流数据的云提供商的多维环境中的智能服务选择
Intelligent Service Selection in a Multi-dimensional Environment of Cloud Providers for IoT stream Data through cloudlets
论文作者
论文摘要
物联网（IoT）服务的扩展以及不同传感器生成的大量数据，表示云计算服务（如存储）的重要性，而不是以往任何时候。物联网流量对云存储服务施加了额外的约束，因为传感器数据预处理功能以及每个数据中心的数据中心和服务器之间的负载平衡。此外，它应该忠于服务质量（QoS）。在这项工作中提出了混合MWG算法，该算法考虑了不同的目标，例如能量，处理时间，传输时间和雾和云层中的负载平衡。 MATLAB脚本用于模拟和实现我们的算法以及不同服务器的服务，例如已经考虑了Amazon，Dropbox，Google Drive等。与Mowca，KGA和NSGAII相比，MWG在间距的度量标准中分别具有7％，13％和25％的提高。此外，与Mowca，KGA和NSGAII相比，MWG的质量度量分别为4％，4.7％和7.3％。总体优化表明，与Mowca，KGA和NSGAII相比，MWG算法的性能分别分别考虑了不同的目标，其性能优于7.8％，17％和21.6％。
The expansion of the Internet of Things(IoT) services and a huge amount of data generated by different sensors, signify the importance of cloud computing services like Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Also, it should be allegiant to the Quality of Service (QoS). The hybrid MWG algorithm has been proposed in this work, which considers different objectives such as energy, processing time, transmission time, and load balancing in both Fog and Cloud Layer. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g. Amazon, Dropbox, Google Drive, etc. have been considered. The MWG has 7%, 13%, and 25% improvement in comparison with MOWCA, KGA, and NSGAII in metric of spacing, respectively. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance in comparison with MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.
