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- W2998534207 abstract "Abstract The Internet of Things generates a massive amount of data through sensors and other physical devices, which cause latency and delay in processing time and response time of smart grid (SG) services. To increase the efficiency of SGs, cloud computing provides a pay‐per model approach to transmit the collected data and enhances the scalability and functionality of end devices. Moreover, in load balancing (LB), resource utilization, and distribution mechanism, milliseconds also make an effect where delays or jitters are not acceptable. Fog computing, an extension of cloud provides computing, networking, storage, communication at the edge of the network, and has overcome the existing challenges of SGs. In this article, a new hybrid model on the highly virtualized platform is proposed. Three algorithms for LB: throttled, round robin, and particle swarm optimization, are analyzed and compared. Moreover, this article also highlights some cost minimization and effective utilization approaches to distribute resources efficiently to provide services in SGs with respect to LB algorithms." @default.
- W2998534207 created "2020-01-10" @default.
- W2998534207 creator A5004251248 @default.
- W2998534207 date "2019-12-27" @default.
- W2998534207 modified "2023-10-17" @default.
- W2998534207 title "Analytic vision on fog computing for effective load balancing in smart grids" @default.
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- W2998534207 doi "https://doi.org/10.1002/ett.3855" @default.
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