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.
翻译:在这项工作中提出了混合 MWG 算法,其中考虑了不同的目标,如能源、处理时间、传输时间、烟雾层和云层的平衡。 MATLAB 脚本用来模拟和实施我们的算法,以及不同服务器的服务,如亚马逊、投篮箱、谷歌驱动器等,已经考虑过。 MWG与MOWCA、KGA和NSGAI相比,在间距方面分别提高了7%、13%和25%。此外,MWG 算法与MOWCA、KGA和NSGAI分别取得了7 %、4.7%和7.3%的质量衡量标准,与MOWCA、KGA和NSGAII相比,在7 % 和 MOGA A中分别取得了更好的业绩,在7 % 和21 % 上,与MGSAA分别取得了更好的业绩分析结果。