International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 16, No. 3, 8 Jun. 2024
Cover page and Table of Contents: PDF (size: 1546KB)
Optimized Extreme Gradient Boosting with Remora Algorithm for Congestion Prediction in Transport Layer
PDF (1546KB), PP.144-158
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Author(s)
Ajay Kumar
1,*
Naveen Hemrajani
1
Index Terms
TCP, Min-Max Normalization, KNN-based Missing Value Imputation, Extreme Gradient Boosting Algorithm (XGBOST), Remora Optimization (ROA)
Abstract
Transmission control protocol (TCP) is the most common protocol found in recent networks to maintain reliable communication. The most popular transport protocol in use today is TCP that cannot fully utilize the ability of the network because of the constraints of its conservative congestion control algorithm and favors reliability over timeliness. Despite congestion is the most frequent cause of lost packets, transmission defects can also result in packet loss. In response to packet loss, end-to-end congestion control mechanism in TCP limits the amount of remarkable, unacknowledged data segments that are permitted in the network. To overcome the drawback, Optimized Extreme Gradient Boosting Algorithm is proposed to predict the congestion. Initially, the data is collected and given to data preprocessing to improve the data quality. Min-Max normalization is used to normalize the data in the particular range and KNN-based missing value imputation is used to replace the missing values in the original data in the preprocessing section. Then the preprocessed data is fed into the Optimized Extreme Gradient Boosting Algorithm to predict the congestion. Remora optimization is used in the designed model for optimally selecting the learning rate to minimize the error for enhancing the prediction accuracy in machine learning. For validating the proposed model, the performance metrics attained by the proposed and existing model are compared. Accuracy, precision, recall and error values for the proposed methods are 96%, 97%, 96% and 3% values are obtained. Thus, the proposed optimized extreme gradient boosting with the remora algorithm for congestion prediction in the transport layer method is the best method than the existing algorithm.
Cite This Paper
Ajay Kumar, Naveen Hemrajani, "Optimized Extreme Gradient Boosting with Remora Algorithm for Congestion Prediction in Transport Layer", International Journal of Computer Network and Information Security(IJCNIS), Vol.16, No.3, pp.144-158 2024. DOI:10.5815/ijcnis.2024.03.10
Reference
[1]K. Wang, Y. Liu, X. Liu, Y. Jing, and S. Zhang, “Adaptive fuzzy funnel congestion control for TCP/AQM network,” ISA transactions, vol. 95, pp. 11-17, 2019.
[2]SJSA. Fathima, T Lalitha, F. Ahmad, and S. Karthick, “Unital Design Based Location Service for Subterranean Network Using Long Range Topology,” Wireless Personal Communications, vol. 124, pp. 1815-1839, 2022.
[3]J. Huang, S. Li, R. Han, and J. Wang, “Receiver-driven fair congestion control for TCP outcast in data center networks,” Journal of Network and Computer Applications, vol. 131, pp. 75-88, 2019.
[4]Y. Bai, and Y. Jing, “Event-triggered network congestion control of TCP/AWM systems,” Neural Computing and Applications, vol. 33, pp. 15877-15886, 2021.
[5]O. Lamrabet, N. El Fezazi, F. El Haoussi, E. H. Tissir, and T. Alvarez, “Congestion control in TCP/IP routers based on sampled-data systems theory,” Journal of Control, Automation and Electrical Systems, vol. 31, pp. 588-596, 2020.
[6]Z. Xu, J. Tang, C. Yin, Y. Wang, and G. Xue, “Experience-driven congestion control: When multi-path TCP meets deep reinforcement learning,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1325-1336, 2019.
[7]S. Karthick, SP. Sankar, and YPA. Teen, “Trust-Distrust Protocol for Secure Routing in Self-Organizing Networks,” In 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), pp. 1-8, 2018.
[8]R. Al-Saadi, G. Armitage, J. But, and P. Branch, “A survey of delay-based and hybrid TCP congestion control algorithms,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3609-3638, 2019.
[9]B. Jaeger, D. Scholz, D. Raumer, F. Geyer, and G. Carle, “Reproducible measurements of TCP BBR congestion control,” Computer Communications, vol. 144, pp. 31-43, 2019.
[10]M. Swarna, and T. Godhavari, “Enhancement of CoAP based congestion control in IoT network-a novel approach,” Materials Today: Proceedings, vol. 37, pp. 775-784, 2021.
[11]A. Kumar, P. V. Srinivas, and A. Govardhan, “A multipath packet scheduling approach based on buffer acknowledgement for congestion control,” Procedia Computer Science, vol. 171, pp. 2137-2146, 2020.
[12]M. R. Kanagarathinam, S. Singh, I. Sandeep, H. Kim, M. K. Maheshwari, J. Hwang, A. Roy, and N. Saxena, “NexGen D-TCP: Next generation dynamic TCP congestion control algorithm,” IEEE Access, vol. 8, pp. 164482-164496, 2020.
[13]L. P. Verma, and M. Kumar, “An IoT based congestion control algorithm,” Internet of Things, vol. 9, pp. 100157, 2020.
[14]N. Makarem, W. B. Diab, I. Mougharbel, and N. Malouch, “On the design of efficient congestion control for the Constrained Application Protocol in IoT,” Computer Networks, vol. 207, pp. 108824, 2022.
[15]W. Wei, K. Xue, J. Han, D. S. Wei, and P. Hong, “Shared bottleneck-based congestion control and packet scheduling for multipath TCP,” IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 653-666, 2020.
[16]N. Akhtar, M. A. Khan, A. Ullah, and M. Y. Javed, “Congestion avoidance for smart devices by caching information in MANETS and IoT,” IEEE Access, vol. 7, pp. 71459-71471, 2019.
[17]M. Polese, F. Chiariotti, E. Bonetto, F. Rigotto, A. Zanella, and M. Zorzi, “A survey on recent advances in transport layer protocols,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3584-3608, 2019.
[18]Z. Zou, Y. Yang, Z. Fan, H. Tang, M. Zou, X. Hu, C. Xiong, and J. Ma, “Suitability of data preprocessing methods for landslide displacement forecasting,” Stochastic Environmental Research and Risk Assessment, vol. 34, pp. 1105-1119, 2020.
[19]W. C. Lin, and C. F. Tsai, “Missing value imputation: a review and analysis of the literature (2006–2017),” Artificial Intelligence Review, vol. 53, pp. 1487-1509, 2020.
[20]H. A. Alamri, and V. Thayananthan, “Bandwidth control mechanism and extreme gradient boosting algorithm for protecting software-defined networks against DDoS attacks,” IEEE Access, vol. 8, pp. 194269-194288, 2020.
[21]H. Jia, X. Peng, and C. Lang, “Remora optimization algorithm,” Expert Systems with Applications, vol. 185, pp. 115665 2021.