Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment
محتوى المقالة الرئيسي
الملخص
The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Abhishek, R., Tipper, D., and Medhi, D., 2018. Network virtualization and survivability of 5g networks: Framework, optimization model, and performance. In IEEE Globecom Workshops (GC Wkshps) pp. 1-6. doi:10.1109/GLOCOMW.2018.8644092.
Addad, R. A., Bagaa, M., Taleb, T., Dutra, D. L. C., and Flinck, H., 2019. Optimization model for cross-domain network slices in 5G networks. IEEE Transactions on Mobile Computing, 19(5), pp. 1156-1169. doi:10.1109/TMC.2019.2905599.
Afolabi, I., Prados-Garzon, J., Bagaa, M., Taleb, T., and Ameigeiras, P., 2019. Dynamic resource provisioning of a scalable E2E network slicing orchestration system. IEEE Transactions on Mobile Computing, 19(11), pp. 2594-2608. doi:10.1109/TMC.2019.2930059.
Ahmed, N., Ngadi, A. B., Sharif, J. M., Hussain, S., Uddin, M., Rathore, M. S., and Zuhra, F. T., 2022. Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction. Sensors, 22(20), pp. 1-34. doi:10.3390/s22207896
AlQahtani, S. A., and Alhomiqani, W. A., 2020. A multi-stage analysis of network slicing architecture for 5G mobile networks. Telecommunication Systems, 73(2), pp. 205-221. doi:10.1007/s11235-019-00607-2
Bohte, S. M., Kok, J. N., and La Poutre, H., 2002. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 48(1-4), pp. 17-37. doi:10.1016/S0925-2312(01)00658-0
Chahlaoui, F., El-Fenni, M. R., and Dahmouni, H., 2019. Performance analysis of load balancing mechanisms in SDN networks. Proceedings of the 2nd International Conference on Networking, Information Systems and Security (NISS19), P. 36, pp. 1-8. doi:10.1145/3320326.3320368
Chergui, H., and Verikoukis, C., 2019. Offline SLA-constrained deep learning for 5G networks reliable and dynamic end-to-end slicing. IEEE Journal on Selected Areas in Communications, 38(2), pp. 350-360. doi:10.1109/JSAC.2019.2959186.
Gupta, A., and Jha, R. K., 2015. A survey of 5G network: Architecture and emerging technologies. IEEE Access, (3), pp. 1206-1232, doi:10.1109/ACCESS.2015.2461602.
Khan, T. A., Mehmood, A., Ravera, J. J. D., Muhammad, A., Abbas, K., and Song, W. C., 2020. Intent-based orchestration of network slices and resource assurance using machine learning. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. pp. 1-2. doi:10.1109/NOMS47738.2020.9110408.
Li, X., Ni, R., Chen, J., Lyu, Y., Rong, Z., and Du, R., 2020. End-to-end network slicing in radio access network, transport network and core network domains. IEEE Access, (8), pp. 29525-29537. doi:10.1109/ACCESS.2020.2972105.
Liang, L., Hao Y., and Geoffrey Y. L., 2018. Toward intelligent vehicular networks: A machine learning framework. IEEE Internet of Things Journal, 6(1), pp. 124-135, doi:10.1109/JIOT.2018.2872122.
Mayoral, A., Vilalta, R., Casellas, R., Martínez, R., and Muñoz, R., 2016. Multi-tenant 5G network slicing architecture with dynamic deployment of virtualized tenant management and orchestration (MANO) instances. ECOC 2016; 42nd European Conference on Optical Communication, Dusseldorf, Germany, pp. 1-3.
Meneses, F., Fernandes, M., Corujo, D., and Aguiar, R. L., 2019. SliMANO: An expandable framework for the management and orchestration of end-to-end network slices. IEEE 8th International Conference on Cloud Networking (CloudNet) pp. 1-6. doi:10.1109/CloudNet47604.2019.9064072.
Oladejo, S. O., and Falowo, O. E., 2017. 5G network slicing: A multi-tenancy scenario. Global Wireless Summit (GWS), pp. 88-92. doi:10.1109/GWS.2017.8300476.
Paropkari, R. A., Beard, C., and Van De Liefvoort, A., 2017. Handover performance prioritization for public safety and emergency networks. In 2017 IEEE 38th Sarnoff Symposium. 8(17), pp. 1-6. doi:10.1109/SARNOF.2017.8080381 .
Ponulak, F., and Kasinski, A., 2011. Introduction to spiking neural networks: Information processing, learning, and applications. Acta neurobiologiae experimentalis, 71(4), pp. 409-433.
Saad, W., Bennis, M., and Chen, M., 2019. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), pp. 134-142. doi:10.1109/MNET.001.1900287 .
Salman, M. I., and Shaker, S. R., 2020. Link Failure Recovery for a Large-Scale Video Surveillance System using a Software-Defined Network. Journal of Engineering, 26(1), pp. 104-120. doi:10.31026/j.eng.2020.01.09.
Saqib, M., Khan, F. Z., Ahmed, M., and Mehmood, R. M., 2019. A critical review on security approaches to software-defined wireless sensor networking. International Journal of Distributed Sensor Networks, 15(12), pp. 1-17. doi:10.1177/1550147719889906.
Shiltagh, N. A., and Naser, M. T., 2015. A Spike Neural Controller for Traffic Load Parameter with Priority-Based Rate in Wireless Multimedia Sensor Networks. Journal of Engineering, 21(11), pp. 192-211. doi:10.31026/j.eng.2015.11.12.
Thantharate, A., Paropkari, R., Walunj, V., and Beard, C., 2019. DeepSlice: A deep learning approach towards an efficient and reliable network slicing in 5G networks. IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 24(5), pp. 0762-0767. doi:10.1109/UEMCON47517.2019.8993066.
Wang, P., Lan, J., and Chen, S., 2014. OpenFlow based flow slice load balancing. China Communications, 11(12), pp. 72-82. doi:10.1109/CC.2014.7019842 .
Walter, F., Röhrbein, F., and Knoll, A., 2015. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Neural Networks, (72), pp. 152-167. doi:10.1016/j.neunet.2015.07.004