Open Access Open Access  Restricted Access Subscription or Fee Access

Examining the Round Trip Time and Packet Length Effect on Window Size by Using the Cuckoo Search Algorithm

Adamu Abubakar(1*), Haruna Chiroma(2), Abdullah Khan(3), Elbara Eldaw Elnour Mohamed(4)

(1) Department of Information Systems, International Islamic University Malaysia, Malaysia
(2) Department of Computer Science, Federal College of Education (Technical), Nigeria
(3) Institute of Business Management Science, Agriculture University Peshawar, Pakistan
(4) Department of Information Systems, International Islamic University Malaysia, Malaysia
(*) Corresponding author



Irregular sequences of inter-arrival times of packet(s) and packet lengths in a network session determine effective traffic performance. Crucial to this is the width of the sliding window. This study utilized raw data from network traffic and built a Neural Network (NN) model trained with the Cuckoo Search (CS) algorithm. Round trip time (RTT) and packet length were captured over several network sessions. They were used as input and their effects were evaluated on window size as the output. Experimental analysis was carried out in order to test the model with various partitioning levels of training and test data. The results of the experiments show that the proposed NN model trained with CS successfully converged without any form of oscillation; the minimum MSE was observed shortly after 100 cycles. The predicted window size and target window size fitted each other. This signifies that the training was successful based on the fitted values of the window size. Thus the proposed model trained with the CS algorithm provides a high convergence rate to the true global minimum and a better optimal solution. Therefore, the combination of CS and NN (CSNN) contributed to decision making on the allocation of window size in determining network flow problems and congestion control.
Copyright © 2016 Praise Worthy Prize - All rights reserved.


Transmission Control Protocol (TCP); Round Trip Time; Packet Length; Window Size

Full Text:



J. Domżał, Z. Duliński, Kantor, M., Rząsa, J., Stankiewicz, R., Wajda, K., & Wójcik, R. (2015). A survey on methods to provide multipath transmission in wired packet networks. Computer Networks, 77, 18-41.

H. Chiroma, S. Abdulkareem, A. Abubakar, A., & Usman, M. J. (2013). Computational intelligence techniques with application to crude oil price projection: a literature survey from 2001-2012. Neural Network World, 23(6), 523.

W. Buchanan, Transmission Control Protocol (TCP) and Internet Protocol (IP). In Applied Data Communications and Networks (pp. 87-109). 1996, Springer US.

K.R. Fall, & Stevens, W. R. TCP/IP illustrated, volume 1: The protocols. addison-Wesley(2011).

H.P Chang, H. P., Kan, H. W., & Ho, M. H. (2012). Adaptive TCP congestion control and routing schemes using cross-layer information for mobile ad hoc networks. Computer Communications, 35(4), 454-474.

Antonello, R., Fernandes, S., Kamienski, C., Sadok, D., Kelner, J., GóDor, I., Szabó, G. and Westholm, T., 2012. Deep packet inspection tools and techniques in commodity platforms: Challenges and trends. Journal of Network and Computer Applications, 35(6), pp.1863-1878.

Li, C. Y., & Wai, P. K. A. (2012, November). Performance comparison of resource reservation schemes in optical packet-switched networks. In Optical Communications and Networks (ICOCN), 2012 11th International Conference on (pp. 24-27). IEEE.

Espi, J., Atkinson, R., Harle, D., Andonovic, I., & Arthur, C. (2010, September). Downlink TCP performance enhancement at handoff for FMIPv6-enabled nodes. In Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on (pp. 2266-2270). IEEE.

Prades, J., Silla, F., Fröning, H., Nüssle, M., & Duato, J. (2015). On the design of a new dynamic credit-based end-to-end flow control mechanism for HPC clusters. Parallel Computing, 46, 32-59.

Cheng, H., Jin, Y., Gao, Y., Yu, Y., Hu, W., & Ansari, N. (2008, May). Per-flow re-sequencing in load-balanced switches by using dynamic mailbox sharing. In Communications, 2008. ICC'08. IEEE International Conference on (pp. 5680-5684). IEEE.

Vlachos, K., Orphanoudakis, T., Papaeftathiou, Y., Nikolaou, N., Pnevmatikatos, D., Konstantoulakis, G., & Sanchez-P, J. A. (2007). Design and performance evaluation of a Programmable Packet Processing Engine (PPE) suitable for high-speed network processors units. Microprocessors and Microsystems, 31(3), 188-199.

Kushwaha, V., & Gupta, R. (2014). Congestion control for high-speed wired network: A systematic literature review. Journal of Network and Computer Applications, 45, 62-78

Jacobson, V. (1988). Congestion avoidance and control. In ACM SIGCOMM computer communication review (Vol. 18, No. 4, pp. 314-329). ACM.

Widmer, J., Denda, R., & Mauve, M. (2001). A survey on TCP-friendly congestion control. Network, IEEE, 15(3), 28-37.

El Khoury, R., Altman, E., & El Azouzi, R. (2010). Analysis of scalable TCP congestion control algorithm. Computer Communications, 33, S41-S49

Zhang, H., Zhou, H., Chen, C., & Dai, G. (2015). Fast fairness convergence through fair rate estimation in Variable-structure congestion Control Protocol. Computer Communications, 70, 54-67.

Delesques, P., Bonald, T., Froc, G., Ciblat, P., & Ware, C. (2013, April). Enhancement of an optical burst switch with shared electronic buffers. In Optical Network Design and Modeling (ONDM), 2013 17th International Conference on (pp. 137-142). IEEE.

Lee, S. S., Li, K. Y., Chan, K. Y., Lai, G. H., & Chung, Y. C. (2014, April). Path layout planning and software based fast failure detection in survivable OpenFlow networks. In Design of Reliable Communication Networks (DRCN), 2014 10th International Conference on the (pp. 1-8). IEEE.

Tanwar, S., Kumar, N., & Rodrigues, J. J. (2015). A systematic review on heterogeneous routing protocols for wireless sensor network. Journal of Network and Computer Applications, 53, 39-56.

Hercog, D. (2002). Generalised sliding window protocol. Electronics Letters, 38(18), 1067-1068.

Khor, I. J., Thomas, J., & Jonyer, I. (2005, January). Sliding Window Protocol for Group Communication in Ad-Hoc Networks. In System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 321c-321c). IEEE.

Cormode, G., & Yi, K. Brief Announcement: Tracking Distributed Aggregates over Time-based Sliding Windows. June 2011 PODC '11: Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing.

Mousavi, H., & Zaniolo, C. (2013, July). Fast computation of approximate biased histograms on sliding windows over data streams. In Proceedings of the 25th International Conference on Scientific and Statistical Database Management (p. 13). ACM

Cooke, P., Fowers, J., Brown, G., & Stitt, G. (2015). A Tradeoff Analysis of FPGAs, GPUs, and Multicores for Sliding-Window Applications. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 8(1), 2.

Ayedemir, M., Bottomley, L., Coffin, M., Jeffries, C., Kiessler, P., Kumar, K., Ligon, W., Marin, J., Nilsson, A., McGovern, J. and Rindos, A. (2001). Two tools for network traffic analysis. Computer Networks, 36(2), 169-179.

Koga, H. Dynamic TCP acknowledgment with sliding window, Theoretical Computer Science 410 (2009) 914-925.

R. Jain. A delay-based approach for congestion avoidance in interconnected heterogeneous computer networks. ACM CCR, 19:56–71, 1989. SIGCOMM’94, pages 24–35, Oct. 1994.

Z. Wang and J. Crowcroft. A new congestion control scheme: Slow start and search (tri-s). ACM Computer Communication Review, 21:32–43, Jan. 1991.

Lawrence S. Brakmo, Sean W. O'Malley, and Larry L. Peterson. 1994. TCP Vegas: new techniques for congestion detection and avoidance. In Proceedings of the conference on Communications architectures, protocols and applications (SIGCOMM '94). ACM, New York, NY, USA, 24-35.

Biaz, S., & Vaidya, N. H. (2003, October). Is the round-trip time correlated with the number of packets in flight? In Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement (pp. 273-278). ACM.

J. Padhye, V. Firoiu, D. Towsley, and J. Krusoe. Modeling TCP throughput: A simple model and its empirical validation. In ACM SIGCOMM ’98 conference on Applications, technologies, architectures, and protocols for computer communication, pages 303–314, Vancouver, CA, 1998.

S. Xu and T. Saadawi. Performance evaluation of TCP algorithms in multi-hop wireless packet networks. Wireless Communications and Mobile Computing, 2(1), 2001

T. Kuang, F. Xiao, and C. Williamson. Diagnosing wireless TCP performance problems: A case study. In Proc. of SPECTS, 2003.

Koutsonikolas, D., Dyaberi, J., Garimella, P., Fahmy, S., & Hu, Y. C. (2007, September). On TCP throughput and window size in a multihop wireless network testbed. In Proceedings of the second ACM international workshop on Wireless network testbeds, experimental evaluation and characterization (pp. 51-58). ACM.

Gebotys, C. H., & White, B. A. (2015). A Sliding Window Phase-Only Correlation Method for Side-Channel Alignment in a Smartphone. ACM Transactions on Embedded Computing Systems (TECS), 14(4), 80.

Xu, W., Zhou, Z., Pham, D. T., Ji, C., Yang, M., & Liu, Q. (2011). Hybrid congestion control for high-speed networks. Journal of Network and Computer Applications, 34(4), 1416-1428.

Othman, M., Ferdosian, N., & Rasul, T. (2015). Rated Window and Packet Size Differentiation Methods for Per-Rate TCP Fairness Over IEEE 802.11. Arabian Journal for Science and Engineering, 40(4), 1057-1067.

Nishio, T., Shinkuma, R., Takahashi, T., & Hasegawa, G. (2011). TCP-based window-size delegation method for TXOP Exchange in wireless local area networks. EURASIP Journal on Wireless Communications and Networking, 2011(1), 1-12.

Barik, R., & Divakaran, D. M. (2012). TCP initial window: a study. In Wired/Wireless Internet Communication (pp. 290-297). Springer Berlin Heidelberg.

Develekos, G., Michail, O., & Douligeris, C. (2003). A Neural Networks Approach to the Estimation of the Retransmission Timer (RTT). In Proc. 9th Panhellenic Conf. in Informatics, Thessaloniki, Greece.

Niu, L. (2015). Applying the Linear Neural Network to TCP Congestion Control, 5th International Conference on Advanced Design and Manufacturing Engineering.

Cortez, P., Rio, M., Rocha, M., & Sousa, P. (2006). Internet traffic forecasting using neural networks. In Neural Networks, 2006. IJCNN'06. International Joint Conference on (pp. 2635-2642). IEEE.

Dondo, M., & Treurniet, J. (2004). Investigation of a neural network implementation of a TCP packet anomaly detection system (No. DRDC-TM-2004-206). Defence Research and Development Canada Ottawa (Ontario).

Cortez, P., Rio, M., Rocha, M., & Sousa, P. (2012). Multi‐scale Internet traffic forecasting using neural networks and time series methods. Expert Systems, 29(2), 143-155.

Rahnami, K., Arabshahi, P., & Gray, A. (2005). Neural network based model reference controller for active queue management of TCP flows. In Aerospace Conference, 2005 IEEE (pp. 1696-1704). IEEE.

Rouhani, M., Tanhatalab, M. R., & Shokohi-Rostami, A. (2010). Nonlinear neural network congestion control based on genetic algorithm for TCP/IP networks. In Computational Intelligence, Communication Systems and Networks (CICSyN), 2010 Second International Conference on (pp. 1-6). IEEE.

Halenár, I., & Libošvárová, A. (2012). The Impact of the Neural Network Structure by the Detection of Undesirable Network Packets. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2).

Cho, H. C., Fadali, M. S., & Lee, H. (2005). Neural network control for TCP network congestion. In American Control Conference, 2005. Proceedings of the 2005 (pp. 3480-3485). IEEE.

Ahmed, W.A.M., Saad, E.S.M., Aziz, E.S.A.: Modified Back Propagation Algorithm for Learning Artificial Neural Networks. In: Eighteenth National Radio Science Conference (NRSC), pp. 345–352 (2001)

Wen, J., Zhao, J.L., Luo, S.W., Han, Z.: The Improvements of BP Neural Network Learning Algorithm. In: 5th Int. Conf. on Signal Processing WCCC-ICSP, pp. 1647–1649 (2000).

Lahmiri, S.: Wavelet transform, neural networks and the prediction of s & p price index: a comparativepaper of back propagation numerical algorithms. Business Intelligence Journal 5(2), 235–244 (2012)

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, World Congress on pp. 210-214 p.

Hercog, D. (2002). Generalised sliding window protocol. Electronics Letters, 38(18), 1067-1068.

Gopal, I., & Kermani, P. (1983). Performance of stop-and-wait protocols over high-delay links. Computer Communications, 6(3), 115-119.

Guérin, R., & Peris, V. (1999). Quality-of-service in packet networks: basic mechanisms and directions. Computer Networks, 31(3), 169-189.

Guth, K., & Ha, T. (1990). An adaptive stop-and-wait ARQ strategy for mobile data communications. In Vehicular Technology Conference, 1990 IEEE 40th (pp. 656-661). IEEE.

Varthis, E. G., & Fotiadis, D. I. (2006). A comparison of stop-and-wait and go-back-N ARQ schemes for IEEE 802.11 e wireless infrared networks. Computer communications, 29(8), 1015-1025.

Rehman, A. U., Yang, L. L., & Hanzo, L. (2015). Performance of Cognitive Hybrid Automatic Repeat reQuest: Stop-and-Wait. In Vehicular Technology Conference (VTC Spring), 2015 IEEE 81st (pp. 1-5). IEEE.

Wang, J. L., & Silvester, J. A. (1991). Performance optimization of the go-back-N ARQ protocols over broadcast channels. Computer Communications, 14(7), 393-404.

Mukherjee, A. (1996). A proof of quasi-independence of sliding window flow control and go-back-n error recovery under independent packet errors. Computer networks and ISDN systems, 28(6), 873-887.

Perreault, S. (2014). Internet Research Task Force (IRTF) M. Demmer Request for Comments: 7242 UC Berkeley Category: Experimental J. Ott.

Liu, Ke, and Jack Lee. "On Improving TCP Performance Over Mobile Data Networks." (2015). Mobile Computing, IEEE Transactions on, 2015, Volume: PP, Issue: 99

Dayhoff, J.E.: Neural-Network Architectures: An Introduction, 1st edn. Van Nostrand Reinhold Publishers, New York (1990)

Craven, M. W., & Shavlik, J. W. (1997). Using neural networks for data mining. Future generation computer systems, 13(2), 211-229.

Nawi, N. M., Khan, A., & Rehman, M. Z. (2013). A new back-propagation neural network optimized with cuckoo search algorithm. In Computational Science and Its Applications–ICCSA 2013 (pp. 413-426). Springer Berlin Heidelberg.

Chiroma, Haruna, Sameem Abdul-Kareem, Abdullah Khan, Nazri Mohd Nawi, Abdulsalam Ya’U. Gital, Liyana Shuib, Adamu I. Abubakar, Muhammad Zubair Rahman, and Tutut Herawan. "Global warming: predicting OPEC carbon dioxide emissions from petroleum consumption using neural network and hybrid cuckoo search algorithm." PloS one 10, no. 8 (2015): e0136140.

Abubakar, A. I., Chiroma, H., & Abdulkareem, S. (2015). Comparing performances of neural network models built through transformed and original data. In Computer, Communications, and Control Technology (I4CT), 2015 International Conference on (pp. 364-369). IEEE

Hair FJ, Black WC, Babin JB, Anderson RE (2010) Multivariate data analysis. Pearson Prentice Hall, New Jersey.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize