Digital Platform for Life Support Management: Topological Concept
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Abstract
In modern digital control platforms, reliability is achieved through the ability to reroute flows in the event of breakdowns and failures. To achieve this, it is possible to use a topological approach that considers the interrelations of elements in various systems, including life support. An overview of existing flow routing algorithms is given; their advantages and disadvantages are given. The paper presents a concept of flow management based on topological tables. Formal concepts of rank (node – edge – face – cell hierarchy) are introduced. For each pair of ranks, a topological contact relation is introduced, and the resulting matrix of sixteen topological tables Tkm records the incidence of elements without using metric information. Such a structure forms a compact, stable connectivity storage, simplifying network updating and analysis. Then, we consider how, based on these concepts, it is possible to organize the storage of connectivity information (topological tables) and use them to reroute data flows in the event of node or channel failures, increasing network reliability. A description of the flow rerouting algorithm is given. After a node or link failure is detected, the corresponding records in the tables are marked as inactive, then the affected sessions are automatically identified, and the shortest bypass route is selected for each. The sequence of operations includes:
1. Prompt table updating.
2. Classification of the affected flows.
3. Search for an alternative path or selection of a previously saved backup.
4. Instant switching to an alternative route without inter-node exchange of service messages.
If backup routes are available, the downtime is limited by the event detection delay; otherwise, the path is calculated using the updated graph with linear complexity.
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Zhao Z., Zhang P., Yang H. Cascading failures in interconnected networks with dynamical redistribution of loads // Physica A: Statistical Mechanics and its Applications. 2015. Vol. 433. Pp. 204–210. DOI: 10.1016/j.physa.2015.03.030 DOI: https://doi.org/10.1016/j.physa.2015.03.030
Gao C., Fan Y., Jiang S., Deng Y., Liu J., Li X. Dynamic robustness analysis of a two-layer rail transit network model // IEEE Transactions on Intelligent Transportation Systems. 2021. Vol. 23. Nо. 7. Pp. 6509–6524. DOI: 10.1109/TITS.2021.3058185 DOI: https://doi.org/10.1109/TITS.2021.3058185
Bejerano Y., Breitbart Y., Orda A., Rastogi R., Sprintson A. Algorithms for computing QoS paths with restoration // IEEE/ACM Transactions on Networking. 2005. Vol. 13. Nо. 3. Pp. 648–661. DOI: 10.1109/TNET.2005.850217 DOI: https://doi.org/10.1109/TNET.2005.850217
Wagner D., Willhalm T. Speed-up techniques for shortest-path computations // Annual Symposium on Theoretical Aspects of Computer Science. 2007. Pp. 23–36. DOI: 10.1007/978-3-540-70918-3_3 DOI: https://doi.org/10.1007/978-3-540-70918-3_3
Dinitz Y., Itzhak R. Hybrid bellman–ford–dijkstra algorithm // Journal of Discrete Algorithms. 2017. Vol. 42. Pp. 35–44. DOI: 10.1016/j.jda.2017.01.001 DOI: https://doi.org/10.1016/j.jda.2017.01.001
Jepsen M.K., Petersen B., Spoorendonk S., Pisinger D. A branch-and-cut algorithm for the capacitated profitable tour problem // Discrete Optimization. 2014. Vol. 14. Pp. 78–96. DOI: 10.1016/j.disopt.2014.08.001 DOI: https://doi.org/10.1016/j.disopt.2014.08.001
Nejad M.M., Mashayekhy L., Chinnam R.B., Phillips A. Hierarchical time-dependent shortest path algorithms for vehicle routing under ITS // Iie Transactions. 2016. Vol. 48. Nо. 2. Pp. 158–169. DOI: 10.1080/0740817X.2015.1078523 DOI: https://doi.org/10.1080/0740817X.2015.1078523
Pathan A.S.K., Monowar M.M., Khan S. Simulation Technologies in Networking and Communications: Selecting the Best Tool for the Test. CRC Press, 2014. DOI: https://doi.org/10.1201/b17650
Alam M.G.R., Suma T.M., Uddin S.M., Siam M.B.A.K., Mahbub M.S.B., Hassan M.M. et al. Queueing theory based vehicular traffic management system through Jackson network model and optimization // IEEE Access. 2021. Vol. 9. Pp. 136018–136031. DOI: 10.1109/ACCESS.2021.3116503 DOI: https://doi.org/10.1109/ACCESS.2021.3116503
Afolalu S.A., Ikumapayi O.M., Abdulkareem A., Emetere M.E., Adejumo O. A short review on queuing theory as a deterministic tool in sustainable telecommunication system // Materials Today: Proceedings. 2021. Vol. 44. Pp. 2884–2888. DOI: 10.1016/j.matpr.2021.01.092 DOI: https://doi.org/10.1016/j.matpr.2021.01.092
Petrović A., Nikolić M., Bugarić U., Delibašić B., Lio P. Controlling highway toll stations using deep learning, queuing theory, and differential evolution // Engineering Applications of Artificial Intelligence. 2023. Vol. 119. P. 105683. DOI: 10.1016/j.engappai.2022.105683 DOI: https://doi.org/10.1016/j.engappai.2022.105683
Dai B., Cao Y., Wu Z., Dai Z., Yao R., Xu Y. Routing optimization meets Machine Intelligence: A perspective for the future network //Neurocomputing. 2021. Vol. 459. Pp. 44–58. DOI: 10.1016/j.neucom.2021.06.093 DOI: https://doi.org/10.1016/j.neucom.2021.06.093
Amin R., Rojas E., Aqdus A., Ramzan S., Casillas-Perez D., Arco J.M. A survey on machine learning techniques for routing optimization in SDN // IEEE Access. 2021. Vol. 9. Pp. 104582–104611. DOI: 10.1109/ACCESS.2021.3099092 DOI: https://doi.org/10.1109/ACCESS.2021.3099092
Sivalingam K.M. Applications of artificial intelligence, machine learning and related techniques for computer networking systems //arXiv preprint arXiv:2105.15103. 2021. DOI: 10.48550/arXiv.2105. 15103
Liang G., Kintak U., Ning X., Tiwari P., Nowaczyk S., Kumar N. Semantics-aware dynamic graph convolutional network for traffic flow forecasting // IEEE Transactions on Vehicular Technology. 2023. Vol. 72. Nо. 6. Pp. 7796–7809. DOI: 10.1109/TVT.2023.3239054 DOI: https://doi.org/10.1109/TVT.2023.3239054
Duan Y., Chen N., Shen S., Zhang P., Qu Y., Yu S. FDSA-STG: Fully dynamic self-attention spatial-temporal graph networks for intelligent traffic flow prediction // IEEE Transactions on Vehicular Technology. 2022. Vol. 71. Nо. 9. Pp. 9250–9260. DOI: 10.1109/TVT.2022.3178094 DOI: https://doi.org/10.1109/TVT.2022.3178094
Srilakshmi U., Veeraiah N., Alotaibi Y., Alghamdi S.A., Khalaf A.I., Subbayamma B.V. An improved hybrid secure multipath routing protocol for MANET // IEEE Access. 2021. Vol. 9. Pp. 163043–163053. DOI: 10.1109/ACCESS.2021.3133882 DOI: https://doi.org/10.1109/ACCESS.2021.3133882
Rozhkov A., Galishnikova V. Explicit Digital Models of Linear Complexes, International // Journal for Computational Civil and Structural Engineering. 2022. Т. 18. Nо. 4. Рр. 101–110. DOI: 10.22337/2587-9618-2022-18-4-101-110. EDN RZOCBU DOI: https://doi.org/10.22337/2587-9618-2022-18-4-101-110
Rozhkov A.N., Galishnikova V.V. Construction and Modification of Topological Tables for Digital Models of Linear Complexes // Mathematical and Computational Applications. 2023. Vol. 28. Nо. 2. P. 37. DOI: 10.3390/mca28020037 DOI: https://doi.org/10.3390/mca28020037
Рожков А. Н., Галишникова В. В. Построение топологических таблиц для цифровых моделей линейных комплексов //BIM-моделирование в задачах строительства и архитектуры. 2023. С. 113–119. DOI: 10.23968/BIMAC.2023.016 DOI: https://doi.org/10.23968/BIMAC.2023.016
Karamela N., Karras D.A. A Comparative Analysis of OSPF and EIGRP Routing Protocol Evaluation // Journal of Transactions in Systems Engineering. 2023. Vol. 1. Nо. 2. Pp. 73–103. DOI: 10.15157/JTSE.2023.1.2.73-103