Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets
Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets
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DOI：
10.3390/en14248601
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发表时间：
2021-12-01
期刊：
影响因子：
3.2
通讯作者：
Nazari-Heris, Morteza
中科院分区：
文献类型：
--
作者：
Milani, Omid Halimi;Motamedi, Seyyed Ahmad;Nazari-Heris, Morteza
物联网(IoT)服务的扩展和不同传感器生成的海量数据表明，存储即服务等云计算服务比以往任何时候都更加重要。物联网流量对云存储服务施加了额外的限制，如传感器数据预处理能力以及数据中心和每个数据中心的服务器之间的负载平衡。此外，服务分配应该与服务质量(Qos)保持一致。本文提出了一种解决存储服务分配中的服务质量问题的算法。提出的混合多目标水循环灰狼优化器(MWG)考虑了雾层和云层不同的服务质量目标(例如，能量、处理时间、传输时间和负载均衡)，这些目标没有被完全解决。本文使用MatLab脚本对算法进行了仿真和实现，并考虑了Amazon、Dropbox、Google Drive等不同服务器的服务。与多目标水循环算法(MOWCA)、基于k-均值的遗传算法(KGA)和非支配排序遗传算法(NSGAII)相比，MWG在间距度量上分别有7%、13%和25%的改进。此外，与MOWCA、KGA和NSGAII相比，MWG在质量度量方面分别优化了4%、4.7%和7.3%。新的混合算法MWG不仅考虑了服务选择中的三个目标，而且与考虑一个或两个目标的工作(S)相比，性能也有所提高。整体优化结果表明，在综合考虑不同目标的情况下，MWG算法的性能分别比MOWCA、KGA和NSGAII算法高7.8%、17%和21.6%。
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.