Intelligent Service Selection in a Multi-dimensional Environment of Cloud Providers for IoT stream Data through cloudlets
This work addresses service selection for IoT data in cloud environments, offering incremental improvements for cloud computing and IoT applications.
The paper tackles the problem of selecting cloud services for IoT stream data by proposing a hybrid MWG algorithm that optimizes objectives like energy, processing time, transmission time, and load balancing. The results show improvements of 7-25% in spacing, 4-7.3% in quality, and 7.8-21.6% in overall performance compared to existing algorithms.
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.