Abstract
The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as 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. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.
Highlights
Cloud computing is a new Internet-based technology that stores data on servers and makes it available to clients on demand
This paper presented a new hybrid multi-objective algorithm for storage service selection in a collaborative and heterogeneous cloud and fog environment
Numerical experiments are conducted to compare the performances of the proposed MWG, multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and NSGAII
Summary
Cloud computing is a new Internet-based technology that stores data on servers and makes it available to clients on demand. Cloud computing has gotten a lot of press in both the corporate and academic worlds. This is a service- and application-related technology that runs on a distributed network, employs virtual resources, and may be accessed via networking and Internet standards [2,3]. The Internet of Things (IoT) and its potential needs have enhanced the importance of cloud computing. The number of cloud-based servers that provide the proper structure for IoT manifestation has expanded. Distributed clouds can be helpful in processing IoT sensors’ data [5]
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