Abstract
Software Defined Networking (SDN) has been preferred over traditional networking due to its dynamic nature in adapting the network structure. This agile nature of SDN imparts non-stationarity in traffic. In this work, we characterize the SDN traffic and study its behavior under dynamic conditions using Augmented Dickey Fuller (ADF) test. Later, we model the SDN under non-stationary conditions using queueing model and solve for average queue length at both controller and switch using Pointwise Stationary Fluid Flow Approximation (PSFFA). The analytical results have been validated through simulations. We develop congestion control algorithm based on (a) Proportional Integral Derivative (PID) control mechanism and (b) Dynamic Random Early Detection (DRED) control mechanism for SDN controller using the fluid flow model. Finally we demonstrate their effectiveness in stabilizing the queue length at the switch and controller under non-stationary conditions. In nut shell our work brings out the importance of the non-stationary behaviour of the traffic in the design and analysis of SDN and its control algorithms.
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Notes
The actual system state (number of packets N) is discrete , with values {0, 1, 2,...} but assumed continuous state space \( [0, \infty ) \) because of fluid flow approximation.
we use PSFFA as against other approximation schemes due to its advantages [26].
References
Tseng CW, Lai PH, Huang BS, Chou LD, Wu MC (2019) NFV deployment strategies in SDN network. Int J High Performance Comput Network 14(2):237–248
Jararweh Y, Alsmirat M, Al-Ayyoub M, Benkhelifa E, Darabseh A, Gupta B, Doulat A (2017) Software-defined system support for enabling ubiquitous mobile edge computing. Comput J 60 (10):1443–1457
Bhushan K, Gupta BB (2019) Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. Journal of Ambient Intelligence and Humanized Computing 10(5):1985–1997
McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Turner J (2008) OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review 38(2):69–74
Poncea OM, Pistirica AS, Moldoveanu F, Asavei V (2019) Design and implementation of an Openflow SDN controller in NS-3 discrete-event network simulator. International Journal of High Performance Computing and Networking 14(1):17–29
Cuong DH, Choi JK, Guha D (2006) Flow-based forwarding scheme and performance analysis in mobile IPv6 networks. IEEE 8th International Conference Advanced Communication Technology 3:6
Beshley M, Seliuchenko M, Panchenko O, Polishuk A (2017) Adaptive flow routing model in SDN. In: IEEE 14th international conference the experience of designing and application of CAD systems in microelectronics (CADSM), pp 298–302
Chiaraviglio L, Mellia M, Neri F (2008) Energy-aware networks: reducing power consumption by switching off network elements. In: FEDERICA-phosphorus tutorial and workshop (TNC2008)
Qian Y, Tipper D (2004) Adaptive channel allocation scheme for next generation wireless networks. IEEE 60th Vehicular Technology Conference 7:4918–4922
Chandrasekaran B (2009) Survey of network traffic models. Waschington University in St. Louis CSE, 567
Jarschel M, Oechsner S, Schlosser D, Pries R, Goll S, Tran-Gia P (2011) Modeling and performance evaluation of an OpenFlow architecture. IEEE 23rd International Teletraffic Congress (ITC), pp 1–7
Mahmood K, Chilwan A, ØSterbø O, Jarschel M (2015) Modelling of OpenFlow-based software-defined networks: the multiple node case. IET Networks 4(5):278–284
Xiong B, Yang K, Zhao J, Li W, Li K (2016) Performance evaluation of OpenFlow-based software-defined networks based on queueing model. Computer Networks 102:172–185
Sood K, Yu S, Xiang Y (2016) Performance analysis of software-defined network switch using M/Geo/1 model. IEEE Commun Lett 20(12):2522–2525
Goto Y, Ng B, Seah WK, Takahashi Y (2019) Queueing analysis of software defined network with realistic openflow–based switch model. Comput Netw 164:106892
Vuppalapati N, Venkatesh TG, Majumder B (2020) Traffic modeling & characterization of software defined networks. In: IEEE international conference on advances in computing and communication engineering (ICACCE), pp 1–6
Miao W, Min G, Wu Y, Wang H, Hu J (2016) Performance modelling and analysis of software-defined networking under bursty multimedia traffic. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12(5s):1–19
Singh D, Ng B, Lai YC, Lin YD, Seah WK (2019) Analytical modelling of software and hardware switches with internal buffer in software-defined networks. Journal of Network and Computer Applications 136:22–37
Adams R (2012) Active queue management: a survey. IEEE Communications Surveys & Tutorials 15(3):1425–1476
MininetProject, http://mininet.org/walkthrough
OpenDayLight Controller, https://docs.opendaylight.org/en/stable-magnesium/
Salman O, Elhajj IH, Kayssi A, Chehab A (2016) SDN controllers: a comparative study. IEEE 18th Mediterranean Electrotechnical Conference (MELECON), pp 1–6
Lamping U, Warnicke E (2004) Wireshark user’s guide. Interface 4(6)
Musaddiq A, Zikria YB, Hahm O, Yu H, Bashir AK, Kim SW (2018) A survey on resource management in IoT operating systems. IEEE Access 6:8459–8482
Cheung YW, Lai KS (1995) Lag order and critical values of the augmented dickey–fuller test. Journal of Business & Economic Statistics 13(3):277–280
Tipper D, Sundareshan MK (1990) Numerical methods for modeling computer networks under nonstationary conditions. IEEE Journal on Selected Areas in Communications 8(9):1682–1695
Medhi J (2002) Stochastic models in queueing theory. Elsevier
Burke PJ (1956) The output of a queuing system. Operations Research 4(6):699–704
Wright WM (2002) Explicit general linear methods with inherent Runge–Kutta stability. Numerical Algorithms 31(1-4):381–399
Zhang X, Papachristodoulou A (2014) A distributed PID controller for network congestion control problems. IEEE American Control Conference, pp 5453–5458
Giglio A (2004) Router-based congestion control through control theoretic active queue management
Bisoy SK, Pattnaik PK (2017) Design of feedback controller for TCP/AQM networks. Eng Sci Technol Int J 20(1):116–132
Visioli A (2006) Practical PID control. Springer Science & Business Media
Cheng M, Wang H, Yan L (2011) Dynamic RED: a modified random early detection. Journal of Computational Information Systems 7(14):5243–5250
Park EC, Lim H, Park KJ, Choi CH (2004) Analysis and design of the virtual rate control algorithm for stabilizing queues in TCP networks. Computer Networks 44(1):17–41
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Vuppalapati, N., Venkatesh, T.G. Modeling & analysis of software defined networks under non-stationary conditions. Peer-to-Peer Netw. Appl. 14, 1174–1189 (2021). https://doi.org/10.1007/s12083-020-01026-w
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DOI: https://doi.org/10.1007/s12083-020-01026-w