Intelligent Service Selection in a Multi-dimensional Environment of Cloud Providers for IoT stream Data through cloudlets

TL;DR

This paper introduces a hybrid MWG algorithm for optimal service selection in cloud environments for IoT data, improving efficiency across multiple metrics compared to existing algorithms.

Contribution

The work proposes a novel hybrid MWG algorithm that considers multiple objectives for service selection in cloud and fog layers, with demonstrated improvements over existing methods.

Findings

MWG outperforms MOWCA, KGA, and NSGAII in spacing metrics.

MWG achieves better quality optimization than comparison algorithms.

Overall, MWG shows significant performance improvements in multi-objective cloud service selection.

Abstract

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.