Abstract
In recent years, the volumes of information processed and transmitted over the network are rapidly increasing, while the demand for speed and quality of transmitted data is also increasing. As a result, there is a need to use edge computing, which allows reducing network response time and more efficiently using bandwidth, as well as significantly improving the performance of the data transmission system. Article describes the application of edge computing in the form of transferring processes to an edge computing cluster, and presents the results of research on existing migration processes, full transfer time and downtime, identifying the optimal migration process for further implementation of the service migration automation algorithm. The following empirical research methods were used in the work: comparative evaluation method and experiment. The work considers the transfer of computational processes to an edge computing cluster; a comparative analysis of methods for automated service migration is carried out; an optimal migration strategy is identified; an algorithm for automating service migration is developed. Solutions related to automation of migration are excellent for use in the field of Internet of Things. They contribute to improving application performance and reducing response time. Automatic service migration allows increasing process efficiency and reducing system maintenance costs.”
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Acknowledgments
The studies at St. Petersburg State University of Telecommunications. Prof. M.A. Bonch-Bruevich were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137.
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Kuznetsov, K., Kuzmina, E., Lapteva, T., Volkov, A., Muthanna, A., Aziz, A. (2024). Service Migration Algorithm for Distributed Edge Computing in 5G/6G Networks. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2023 2023. Lecture Notes in Computer Science, vol 14542. Springer, Cham. https://doi.org/10.1007/978-3-031-60994-7_27
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