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


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DOI: https://doi.org/10.15866/irecos.v11i9.9708

Abstract


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.
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Keywords


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

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References


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.
http://dx.doi.org/10.1016/j.comnet.2014.12.001

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.
http://dx.doi.org/10.14311/nnw.2013.23.032

W. Buchanan, Transmission Control Protocol (TCP) and Internet Protocol (IP). In Applied Data Communications and Networks (pp. 87-109). 1996, Springer US.
http://dx.doi.org/10.1007/978-1-4613-1207-9_5

K.R. Fall, & Stevens, W. R. TCP/IP illustrated, volume 1: The protocols. addison-Wesley(2011).
http://dx.doi.org/10.1109/mnet.1996.539855

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.
http://dx.doi.org/10.1016/j.comcom.2011.11.008

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.
http://dx.doi.org/10.1016/j.jnca.2012.07.010

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.
http://dx.doi.org/10.1109/icocn.2012.6486239

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.
http://dx.doi.org/10.1109/pimrc.2010.5671691

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.
http://dx.doi.org/10.1016/j.parco.2015.03.006

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.
http://dx.doi.org/10.1109/icc.2008.1064

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.
http://dx.doi.org/10.1016/j.micpro.2006.09.001

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
http://dx.doi.org/10.1016/j.jnca.2014.07.005

Jacobson, V. (1988). Congestion avoidance and control. In ACM SIGCOMM computer communication review (Vol. 18, No. 4, pp. 314-329). ACM.
http://dx.doi.org/10.1145/52325.52356

Widmer, J., Denda, R., & Mauve, M. (2001). A survey on TCP-friendly congestion control. Network, IEEE, 15(3), 28-37.
http://dx.doi.org/10.1109/65.923938

El Khoury, R., Altman, E., & El Azouzi, R. (2010). Analysis of scalable TCP congestion control algorithm. Computer Communications, 33, S41-S49
http://dx.doi.org/10.1016/j.comcom.2010.04.031

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.
http://dx.doi.org/10.1016/j.comcom.2015.08.008

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.
http://dx.doi.org/10.1109/pho.2011.6110548

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.
http://dx.doi.org/10.1109/drcn.2014.6816141

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.
http://dx.doi.org/10.1016/j.jnca.2015.03.004

Hercog, D. (2002). Generalised sliding window protocol. Electronics Letters, 38(18), 1067-1068.
http://dx.doi.org/10.1049/el:20020703

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.
http://dx.doi.org/10.1109/hicss.2005.544

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.
http://dx.doi.org/10.1145/1993806.1993839

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
http://dx.doi.org/10.1145/2484838.2484851

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.
http://dx.doi.org/10.1145/2659000

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.
http://dx.doi.org/10.1016/s1389-1286(00)00188-2

Koga, H. Dynamic TCP acknowledgment with sliding window, Theoretical Computer Science 410 (2009) 914-925.
http://dx.doi.org/10.1016/j.tcs.2008.12.017

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.
http://dx.doi.org/10.1145/74681.74686

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.
http://dx.doi.org/10.1145/116030.116033

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.
http://dx.doi.org/10.1145/190314.190317

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.
http://dx.doi.org/10.1145/948205.948240

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.
http://dx.doi.org/10.1145/285237.285291

S. Xu and T. Saadawi. Performance evaluation of TCP algorithms in multi-hop wireless packet networks. Wireless Communications and Mobile Computing, 2(1), 2001
http://dx.doi.org/10.1002/wcm.35

T. Kuang, F. Xiao, and C. Williamson. Diagnosing wireless TCP performance problems: A case study. In Proc. of SPECTS, 2003.
http://dx.doi.org/10.1109/glocom.1997.644585

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.
http://dx.doi.org/10.1145/1287767.1287778

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.
http://dx.doi.org/10.1145/2783441

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.
http://dx.doi.org/10.1016/j.jnca.2011.03.025

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.
http://dx.doi.org/10.1007/s13369-015-1580-6

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.
http://dx.doi.org/10.1186/1687-1499-2011-58

Barik, R., & Divakaran, D. M. (2012). TCP initial window: a study. In Wired/Wireless Internet Communication (pp. 290-297). Springer Berlin Heidelberg.
http://dx.doi.org/10.1007/978-3-642-30630-3_25

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.
http://dx.doi.org/10.1109/pci.2011.14

Niu, L. (2015). Applying the Linear Neural Network to TCP Congestion Control, 5th International Conference on Advanced Design and Manufacturing Engineering.
http://dx.doi.org/10.2991/icadme-15.2015.113

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.
http://dx.doi.org/10.1109/ijcnn.2006.247142

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).
http://dx.doi.org/10.13031/2013.16904

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.
http://dx.doi.org/10.1111/j.1468-0394.2010.00568.x

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.
http://dx.doi.org/10.1109/aero.2005.1559464

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.
http://dx.doi.org/10.1109/cicsyn.2010.21

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).
http://dx.doi.org/10.1109/csie.2009.471

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.
http://dx.doi.org/10.1109/acc.2005.1470511

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)
http://dx.doi.org/10.1109/nrsc.2001.929244

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).
http://dx.doi.org/10.1109/icosp.2000.893417

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)
http://dx.doi.org/10.1016/b978-0-444-89488-5.50171-8

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, World Congress on pp. 210-214 p.
http://dx.doi.org/10.1109/nabic.2009.5393690

Hercog, D. (2002). Generalised sliding window protocol. Electronics Letters, 38(18), 1067-1068.
http://dx.doi.org/10.1049/el:20020703

Gopal, I., & Kermani, P. (1983). Performance of stop-and-wait protocols over high-delay links. Computer Communications, 6(3), 115-119.
http://dx.doi.org/10.1016/0140-3664(83)90001-4

Guérin, R., & Peris, V. (1999). Quality-of-service in packet networks: basic mechanisms and directions. Computer Networks, 31(3), 169-189.
http://dx.doi.org/10.1016/s0169-7552(98)00261-x

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.
http://dx.doi.org/10.1109/vetec.1990.110400

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.
http://dx.doi.org/10.1016/j.comcom.2005.06.021

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.
http://dx.doi.org/10.1109/vtcspring.2015.7146009

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.
http://dx.doi.org/10.1016/0140-3664(91)90028-y

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.
http://dx.doi.org/10.1016/0169-7552(95)00064-x

Perreault, S. (2014). Internet Research Task Force (IRTF) M. Demmer Request for Comments: 7242 UC Berkeley Category: Experimental J. Ott.
http://dx.doi.org/10.17487/rfc5743

Liu, Ke, and Jack Lee. "On Improving TCP Performance Over Mobile Data Networks." (2015). Mobile Computing, IEEE Transactions on, 2015, Volume: PP, Issue: 99
http://dx.doi.org/10.1109/wcnc.2013.6554870

Dayhoff, J.E.: Neural-Network Architectures: An Introduction, 1st edn. Van Nostrand Reinhold Publishers, New York (1990)
http://dx.doi.org/10.1002/ange.19911030953

Craven, M. W., & Shavlik, J. W. (1997). Using neural networks for data mining. Future generation computer systems, 13(2), 211-229.
http://dx.doi.org/10.1016/s0167-739x(97)00022-8

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.
http://dx.doi.org/10.1007/978-3-642-39637-3_33

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.
http://dx.doi.org/10.1371/journal.pone.0136140

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
http://dx.doi.org/10.1109/i4ct.2015.7219599

Hair FJ, Black WC, Babin JB, Anderson RE (2010) Multivariate data analysis. Pearson Prentice Hall, New Jersey.
http://dx.doi.org/10.1079/9781845931308.0059


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