Abstract
Machine learning advanced tactics provide flexible assistance to organize, maintain, and optimize SDN flat topology, despite that intelligence is hard to apply and deployed in the SDN domain. Knowledge-defined network modeling opens a new challengeable door to build a self-driving SDN framework with better precision and efficient performance, all of this encourages the proposal of a novel EQUI-dispatcher model for flat topology. The model is designed on the advanced learning capability of gated graph recurrent neural networks (GG-Rec-NNs). Our proposed model has a generalized capability on variable traffic load and delays under disparate routing schemes. In GG-Rec-NNs several learnable parameters are liberated size and the sequence's length over arbitrary topologies. The graph illustrates the secular structure of sequence index and spatial structure support, which guaranteed scalability. Recurrent neural network (RNN) is used to capture hidden state dependencies on a sequence of variable size for link-level message aggregation, along with this vanishing gradient problem come across. To overcome this problem, gate (on Link and Path) is embedded to encode long-range dependencies, during training traffic data and routing not visible when a test against topologies. We also present model potential in the light of numerical experiments or results based on real and synthetic datasets that support its feasibility as compared to traditional routing strategies for SDN networks.
Similar content being viewed by others
References
Lecun Y (2015) Deep learning & convolutional networks. 2015 IEEE Hot Chips 27 Symposium (HCS)
Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38(8):10389–10397
Harshit K, Singh D, Tiwari S, Kaur M, Jeong C-W, Nam Y, Khan MA (2021) Screening of COVID-19 patients using deep learning and IoT framework. Cmc-Comput Mater Continua 69(3):3459–3475
Sultan S, Javaid Q, Malik AJ, Al-Turjman F, Attique M (2021) Collaborative-trust approach toward malicious node detection in vehicular ad hoc networks. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01632-5
Jillani AG, Shah JH, Sharif M, Tariq U, Akram T (2021) A non-blind deconvolution semi pipelined approach to understand text in blurry natural images for edge intelligence. Inf Process Manag 58(6):102675
Mazhar Rathore M, Paul A, Rho S, Murad Khan S, Vimal S, Shah SA (2021) Smart traffic control: Identifying driving-violations using fog devices with vehicular cameras in smart cities. Sustain Cities Soc 71:102986. https://doi.org/10.1016/j.scs.2021.102986
Battaglia PW et al. (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261
Saeed R, Rubab S, Asif S, Khan MM, Murtaza S, Kadry S, Nam Y (2021) An automated system to predict popular cybersecurity news using document embeddings. Comput Model Eng Sci 127(2):533–547
Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.2981333
Rusek K, Suárez-Varela J, Mestres A, Barlet-Ros P, Cabellos-Aparicio A (2019) Unveiling the potential of graph neural networks for network modeling and optimization in SDN. In: Proceedings of the 2019 ACM Symposium on SDN Research
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2020) Message passing neural networks. In: Schütt KT, Stefan Chmiela O, von Lilienfeld A, Tkatchenko A, Tsuda K, Müller K-R (eds) Machine learning meets quantum physics. Springer, Cham, pp 199–214. https://doi.org/10.1007/978-3-030-40245-7_10
Nettleton DF (2013) Data mining of social networks represented as graphs. Comput Sci Rev 7:1–34
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib L, Ventola P, Duncan J (2020) BrainGNN: interpretable brain graph neural network for fMRI analysis
Rauf HT, Ikram Ullah Lali M, Khan MA, Kadry S, Alolaiyan H, Razaq A, Irfan R (2021) Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-020-01494-0
Rauf HT, Shoaib U, Lali MI, Alhaisoni M, Irfan MN, Khan MA (2020) Particle swarm optimization with probability sequence for global optimization. IEEE Access 8:110535–110549. https://doi.org/10.1109/ACCESS.2020.3002725
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning, ser. The Adaptive Computation and Machine Learning Series. Cambridge, MA: The MIT Press
Kanwal S, Zeshan I, Fadi A-T, Aun I, Muhammad AK (2021) Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter. Inf Process Manag 58(5):102676
Battaglia PW, Hamrick JB, Bapst V, Alvaro S, Vinicius Z, Mateusz M, Andrea T, David R, Adam S, Ryan F, et al. (2018) Relational inductive biases, deep learning, and graph networks. ArXiv preprint arXiv: 1806.01261
Feamster N, Rexford J, Zegura E (2014) The road to SDN. ACM SIGCOMM Comput Commun Rev 44(2):87–98
Yeganeh SH, Tootoonchian A, Ganjali Y (2013) On scalability of software-defined networking. IEEE Commun Mag 51(2):136–141
Azodolmolky S, Wieder P, Yahyapour R (2013) Performance evaluation of a scalable software-defined networking deployment. In: Proceedings of the 2nd Eur. Workshop Softw. Defined Netw. Oct. 2013, pp 68–74
Benson T, Akella A, Maltz DA (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM Conf. Internet Meas. (IMC), 2010, pp 267–280
Canini M et al (2014) STN: a robust and distributed sdn control plane. Open Netw. Summit Res, Track
Koponen T et al (2010) Onix: A distributed control platform for large-scale production networks. In: Proceedings of the 9th USENIX Conf. Oper. Syst. Design Implement. (OSDI), 2010, pp 351–364
Tootoonchian A, Ganjali Y (2010) HyperFlow: a distributed control plane for OpenFlow. In: Proceedings of the Internet Netw. Manag. Conf. Res. Enterprise Netw. (INM/WREN), 2010, p 3
Berde P et al. (2014) ONOS: Towards an open, distributed SDN OS. In: Proceedings of the 3rd Workshop Hot Topics Softw. Defined Netw. (HotSDN), Chicago, IL, USA, 2014, pp 1–6
Sherwood R, Gibb G, Yap K-K, Appenzeller G, Casado M, McKeown N, Parulkar G (2009) Flowvisor: a network Virtualization layer. Open Switch Consort Tech Rep, 1–13
Phemius K, Bouet M, Leguay J (2013) Disco: distributed multi-domain SDN controllers. In: Proceedings of the Network Operations and Management Symposium, pp 1–4
Phemius K, Bouet M, Leguay J (2013) DISCO: distributed multidomain SDN controllers. CoRR, vol. abs/1308.6138, Aug. 2013
Samaan N, Karmouch A (2009) Towards autonomic network management: an analysis of current and future research directions. IEEE Commun Surv Tuts 11(3):22–36
Clark D, et al. (2003) A knowledge plane for the internet. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. ACM
Kreutz D et al (2015) Software-defined networking: a comprehensive survey. Proc IEEE 103(1):14–76
Kim C, Sivaraman A et al (2015) In-band network telemetry via programmable data planes. Industrial demo, ACM SIGCOMM
Mestres A, Rodriguez-Natal A, Carner J, BarletRos P, Alarcón E et al (2017) Knowledge-defined networking. SIGCOMM Comput Commun Rev 4(3):2–10
Mowei W, Yong C, Xin W, Shihan X, Junchen J (2018) Machine learning for networking: Workflow, advances and opportunities. IEEE Network 32(2):92–99
Shihan X, Dongdong H, Zhibo G (2018) Deep-Q: trafficdriven QoS inference using deep generative network. In: Proceedings of the Workshop on Network Meets AI & ML. ACM, pp 67–73
Albert M, Eduard A, Yusheng J, Albert C (2018) Understanding the modeling of computer network delays using neural networks. In: Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. ACM, pp 46–52
Fabien G, Georg C (2018) Learning and generating distributed routing protocols using graph-based deep learning. In: Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. ACM, pp 40–45
Asaf V, Michael S, Dafna S, Aviv T (2017) Learning to route. In: Proceedings of the 16th ACM Workshop on Hot Topics in Networks (HotNets- XVI). ACM, New York, NY, USA, pp 185–191
Sainbayar S, Rob F, et al. (2016) Learning multiagent communication with backpropagation. In Advances in Neural Information Processing Systems. Pp 2244–2252
Justin AB, Michael LL (1994) Packet routing in dynamically changing networks: a reinforcement learning approach. In: Cowan JD, Tesauro G, Alspector J (eds) Advances in neural information processing systems, 6. Morgan-Kaufmann, pp 671–678
David W, Kagan T, Jeremy F (1999) Using collective intelligence to route internet traffic. In: Kearns MJ, Solla SA, Cohn DA (eds) Advances in neural information processing systems 11. MIT Press, pp 952–960
Leonid P, Virginia S (2002) Reinforcement learning for adaptive routing. In: Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1825–1830
Jakob NF, Yannis MA, Nando de F, Shimon W (2016) Learning to communicate to solve riddles with deep distributed recurrent Q-Networks. (Feb. 2016). arXiv:cs.AI/1602.02672v1
Fabien G (2017) Performance evaluation of network topologies using graph based deep learning. In: Proceedings of the 11th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2017)
Baz A (2018) Bayesian machine learning algorithm for flow prediction in SDN switches. In: Proceedings of the 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 4–6 April 2018; Pp 1–7
Yu C, Lan J, Guo Z, Hu Y (2018) DROM: optimizing the routing in software-defined networks with deep reinforcement learning. IEEE Access 6:64533–64539
Yang H, Ivey J, Riley GF (2017) Scalability comparison of SDN control plane architectures based on simulations. In: Proceedings of the 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC)
Casado M, Freedman MJ, Pettit J, Luo J, McKeown N, Shenker S (2007) Ethane. In: Proceedings of the 2007 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications - SIGCOMM '07
Gude N, Koponen T, Pettit J, Pfaff B, Casado M, McKeown N, Shenker S (2008) NOX. ACM SIGCOMM. Comput Commun Rev 38(3):105–110
Dixit AA, Hao F, Mukherjee S, Lakshman TV, Kompella R (2014) ElastiCon. In: Proceedings of the Tenth ACM/IEEE Symposium on Architectures for Networking and Communications Systems - ANCS '14
Phemius K, Bouet M, Leguay J (2014) DISCO: distributed multi-domain SDN controllers. In: Proceedings of the 2014 IEEE Network Operations and Management Symposium (NOMS)
Ahmed M, Muhammad R, Hikmat UK, Saqib I, Jung-In C, Yunyoung N, Seifedine K (2021) Real-time violent action recognition using key frames extraction and deep learning. CMC-Comput Mater Continua 69(2):2217–2230
Attique KM, Alhaisoni M, Armghan A, Alenezi F, Tariq U, Nam Y, Akram T (2021) Video analytics framework for human action recognition. CMC-Comput Mater Continua 68(3):3841–3859
McCauley J, Harchol Y, Panda A, Raghavan B, Shenker S (2019) Enabling a permanent revolution in internet architecture. In: Proceedings of the ACM Special Interest Group on Data Communication
Almadhor A, Rauf HT, Khan MA, Kadry S, Nam Y (2021) A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid. Energy Reports 7:7906–7912. https://doi.org/10.1016/j.egyr.2021.01.034
Khan MA, Muhammad K, Sharif M, Akram T, de Albuquerque HCV (2021) Multi-class skin lesion detection and classification via teledermatology. IEEE J Biomed Health Inf 25(12):4267–4275. https://doi.org/10.1109/JBHI.2021.3067789
Curtis AR, Mogul JC, Tourrilhes J, Yalagandula P, Sharma P, Banerjee S (2011) DevoFlow. In: Proceedings of the ACM SIGCOMM 2011 Conference on SIGCOMM - SIGCOMM '11”
Vissicchio S, Tilmans O, Vanbever L, Rexford J (2015) Central control over distributed routing. ACM SIGCOMM Comput Commun Rev 45(4):43–56
Tootoonchian A, Ghobadi M, Ganjali Y (2010) OpenTM: traffic matrix estimator for openflow networks. In: Krishnamurthy A, Plattner B (eds) Passive and active measurement. Springer, Berlin, Heidelberg, pp 201–210. https://doi.org/10.1007/978-3-642-12334-4_21
“Onix: A Distributed Control Platform for Large-scale ... (n.d.)”. http://yuba.stanford.edu/~casado/onix-osdi.pdf
Yeganeh SH, Tootoonchian A, Ganjali Y (2013) On scalability of software-defined networking. IEEE\Commun Mag 51(2):136–141
Erickson D (2013) The beacon openflow controller. In: Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking – HotSDN '13
Akiyama T, Teranishi Y, Banno R, Iida K, Kawai Y (2016) Scalable pub/sub system using openflow control. J Inf Process 24(4):635–646
Zhao B, Zhao J, Wang X, Wolf T (2019) Ruletailor: optimizing flow table updates in openflow switches with rule transformations. IEEE Trans Netw Serv Manage 16(4):1581–1594
Wang C, Youn HY (2019) Entry aggregation and early match using hidden markov model of flow table in SDN. Sensors 19(10):2341
Baz A (2018) Bayesian machine learning algorithm for flow prediction in SDN switches. In: Proceedings of the 2018 1st International Conference on Computer Applications & Information Security (ICCAIS)
Ahsan W, Khan MF, Aadil F, Maqsood M, Ashraf S, Nam Y, Rho S (2020) Optimized node clustering in VANETs by using meta-heuristic algorithms. Electronics 9(3):394. https://doi.org/10.3390/electronics9030394
András V (2001) The OMNeT++ discrete event simulation system. In: Proceedings of the European Simulation Multiconference (ESM’2001)
Fatimah Audah MZ, Chin TS, Zulfadzli Y, Lee CK, Rizaluddin K (2019) Towards efficient and scalable machine learning-based QoS traffic classification in software-defined network. In: Awan I, Younas M, Ünal P, Aleksy M (eds) Mobile Web and Intelligent Information Systems: 16th International Conference, MobiWIS 2019, Istanbul, Turkey, August 26–28, 2019, Proceedings. Springer, Cham, pp 217–229. https://doi.org/10.1007/978-3-030-27192-3_17
Alzu’bi A, Amira A, Ramzan N (2016) Compact root bilinear CNNs for content-based image retrieval. In: Proceedings of the 2016 International Conference on Image, Vision and Computing (ICIVC), Portsmouth, UK, 3–5 August 2016; pp 41–45
Alzu’bi A, Amira A, Ramzan N, Jaber T (2016) Improving content-based image retrieval with compact global and local multi- features. Int J Multimed Inf Retr 5:237–253
Knowledge-defined networking. (n.d.). Retrieved March 31, 2021, from https://github.com/knowledgedefinednetworking
What is LSTM? A basic overview for 2021. (2021, March 02). Retrieved April 06, 2021, from https://www.jigsawacademy.com/blogs/data-science/lstm
Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural\networks. In Proc, NIPS, p 2017
Science O (2019) 20 open datasets for natural language processing. Retrieved April 13, 2021, from https://medium.com/@ODSC/20-open-datasets-for-natural-language-processing-538fbfaf8e38
Imagenet database, http://www.image-net.org/
Xiaojun H, Jun Z, Brahim B, Chi-Chung C (2004) Wavelength converter placement in least-load-routing-based optical networks using genetic algorithms. J Opt Netw 3(5):363–378
Pedro J, Santos J, Pires J (2011) Performance evaluation of integrated OTN/DWDM networks with single-stage multiplexing of optical channel data units. In: Proceedings of the 2011 13th International Conference on Transparent Optical Networks
Fernando B, Emílio CGW, Luiz N Jr (2012) Fast emergency paths schema to overcome transient link failures in OSPF routing. arXiv preprint arXiv: 1204.2465
Http://knowledgedefinednetworking.org/data/datasets_v0/synth50.tar.gz. (n.d.)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest in this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ashraf, A., Iqbal, Z., Khan, M.A. et al. Scalable offloading using machine learning methods for distributed multi-controller architecture of SDN networks. J Supercomput 78, 10191–10210 (2022). https://doi.org/10.1007/s11227-022-04313-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-022-04313-w