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Challenges of Traditional Networks and Development of Programmable Networks

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Software Defined Internet of Everything

Part of the book series: Internet of Things ((ITTCC))

Abstract

The life cycle of a network system usually includes four stages: demand investigation, planning and design, deployment and implementation, and operation and maintenance. Based on this cycle, a huge network architecture has now been formed, which has played an important role in promoting economic and social development. However, with the vigorous rise of technologies such as big data, cloud computing, Internet of Things, and mobile Internet, Internet applications are becoming increasingly diversified and business volume is increasing. Therefore, the current network architecture is gradually unable to meet the demand, and the existing problems are becoming increasingly prominent. In general, the core problem is that there is a contradiction between the diverse and changeable network upper-layer applications and business requirements and the current stable and rigid traditional network architecture. In order to meet a specific application requirement, it usually needs to include a large number of hardware devices. However, a noteworthy problem is that network devices produced by different manufacturers usually require different ways to debug and configure. Therefore, in a network that mixes equipment from multiple different vendors, managing and deploying the network is a very big challenge. Moreover, the inability to perform intelligent flow control and visualized network status supervision based on network conditions is also a problem that hinders further development. Based on the above problems, software-defined networking (SDN) is a better solution. In general, SDN has the following three advantages: (1) SDN can change the tightly coupled architecture of applications and networks under traditional networks and improve the level of network resource pooling; (2) SDN networks can realize automatic network deployment and configuration, and support rapid business launch and flexible expansion; (3) By introducing programmable features, automated network services and protocol scheduling can be realized. However, the architecture still has some challenges worth considering, such as: (1) Challenges faced by interface/protocol standardization. At present, the control architecture system of the SDN centralized control concept is not unified, and it is difficult to achieve mutual operation due to the different degrees of vendors’ support for the SDN standard. (2) Security challenges. The core controller of the SDN network may have security problems such as excessive load, single point failure, and vulnerability to network attacks. Therefore, it is necessary to establish a reasonable mechanism to ensure the safe and stable operation of the entire system. (3) Challenges in performance. The existing ASIC chip architecture is based on the traditional IP or Ethernet addressing and forwarding design. Therefore, whether the equipment under the SDN architecture can maintain the theoretical high performance remains to be discussed. To sum up, this chapter will start from the analysis and comparison of the traditional network architecture and the SDN network architecture, summarize the problems in the traditional architecture and the necessity of the development of the SDN architecture, and further analyze the application scenarios and the existence of the SDN architecture challenge.

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References

  1. Aujla, G. S., & Kumar, N. (2018). SDN-based energy management scheme for sustainability of data centers: An analysis on renewable energy sources and electric vehicles participation. Journal of Parallel and Distributed Computing, 117, 228–245.

    Article  Google Scholar 

  2. Aujla, G. S., Jindal, A., Kumar, N., & Singh, M. (2016). SDN-based data center energy management system using res and electric vehicles. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6).

    Google Scholar 

  3. Aujla, G. S., Chaudhary, R., Kumar, N., Kumar, R., & Rodrigues, J. J. P. C. (2018). An ensembled scheme for QoS-aware traffic flow management in software defined networks. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–7). IEEE.

    Google Scholar 

  4. Aujla, G. S., Jindal, A., & Kumar, N. (2018). EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system. Computer Networks, 143, 247–262.

    Article  Google Scholar 

  5. Aujla, G. S., Chaudhary, R., Kaur, K., Garg, S., Kumar, N., & Ranjan, R. (2018). SAFE: SDN-assisted framework for edge–cloud interplay in secure healthcare ecosystem. IEEE Transactions on Industrial Informatics, 15(1), 469-480.

    Article  Google Scholar 

  6. Aujla, G. S. S., Kumar, N., Garg, S., Kaur, K., & Ranjan, R. (2019). EDCSuS: Sustainable edge data centers as a service in SDN-enabled vehicular environment. IEEE Transactions on Sustainable Computing, 1–1.

    Google Scholar 

  7. Aujla, G. S., Singh, A., & Kumar, N. (2020). Adaptflow: Adaptive flow forwarding scheme for software-defined industrial networks. IEEE Internet of Things Journal, 7(7), 5843–5851.

    Article  Google Scholar 

  8. Aujla, G. S., Singh, A., Singh, M., Sharma, S., Kumar, N., & Choo, K. R. (2020). Blocked: Blockchain-based secure data processing framework in edge envisioned v2x environment. IEEE Transactions on Vehicular Technology, 69(6), 5850–5863.

    Article  Google Scholar 

  9. Aujla, G. S., Singh, M., Bose, A., Kumar, N., Han, G., & Buyya, R. (2020). Blocksdn: Blockchain-as-a-service for software defined networking in smart city applications. IEEE Network, 34(2), 83-91.

    Article  Google Scholar 

  10. Budhiraja, I., Kumar, N., Tyagi, S., Tanwar, S., & Obaidat, M. S. (2020). URJA: Usage jammer as a resource allocation for secure transmission in a CR-NOMA-based 5g Femtocell system. IEEE Systems Journal, 1–10.

    Google Scholar 

  11. Garg, S., Singh, A., Aujla, G. S., Kaur, S., Batra, S., & Kumar, N. (2020). A probabilistic data structures-based anomaly detection scheme for software-defined internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 1–10.

    Google Scholar 

  12. Kumar, N., & Kumar, M. (2015). Closely spacified wide dual-band microstrip band pass filter using coupled stepped-impedance resonators. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 865–867).

    Google Scholar 

  13. Kumar, N., & Tripathi, M. M. (2017). Evaluation of effectiveness of ANN for feature selection based electricity price forecasting. In 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT) (pp. 1–5).

    Google Scholar 

  14. Kumar, N., Chilamkurti, N., Zeadally, S., & Jeong, Y. (2014). Achieving quality of service (QoS) using resource allocation and adaptive scheduling in cloud computing with grid support. The Computer Journal, 57(2), 281–290.

    Article  Google Scholar 

  15. Kumar, N., Vinoy, K. J., & Gopalakrishnan, S. (2018). Improved well-conditioned model order reduction method based on multilevel Krylov subspaces. IEEE Microwave and Wireless Components Letters, 28(12), 1065–1067.

    Article  Google Scholar 

  16. Neeraj, N., Naresh, M., Yadav, A. K., & Mathew, L. (2019). Effect of statcom on integration of renewable energy generation with the main grid. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) (vol. 1, pp. 1–5).

    Google Scholar 

  17. Singh, A., Aujla, G. S., & Bali, R. S. (2020). Intent-based network for data dissemination in software-defined vehicular edge computing. IEEE Transactions on Intelligent Transportation Systems, 1–9.

    Google Scholar 

  18. Singh, A., Aujla, G. S., Singh Bali, R., Chahal, P. K., & Singh, M. (2020). A self organised workload classification and scheduling approach in IoT-edge-cloud ecosystem. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) (pp. 1–5).

    Google Scholar 

  19. Singh, M., Aujla, G. S., & Bali, R. S. (2020). A deep learning-based blockchain mechanism for secure internet of drones environment. IEEE Transactions on Intelligent Transportation Systems, 1–10.

    Google Scholar 

  20. Singh, P., Kaur, A., Aujla, G. S., Batth, R. S., & Kanhere, S. (2020). Daas: Dew computing as a service for intelligent intrusion detection in edge-of-things ecosystem. IEEE Internet of Things Journal, 1–1.

    Google Scholar 

  21. Singh, A., Batra, S., Aujla, G. S., Kumar, N., & Yang, L. T. (2020). BloomStore: dynamic bloom-filter-based secure rule-space management scheme in SDN. IEEE Transactions on Industrial Informatics, 16(10), 6252–6262. https://doi.org/10.1109/TII.2020.2966708.

    Article  Google Scholar 

  22. Sood, K., Karmakar, K. K., Varadharajan, V., Kumar, N., Xiang, Y., & Yu, S. (2021). Plug-in over plug-in (pop) evaluation in heterogeneous 5g enabled networks and beyond. IEEE Network, 1–7.

    Google Scholar 

  23. Vangala, A., Bera, B., Saha, S., Das, A. K., Kumar, N., & Park, Y. H. (2020). Blockchain-enabled certificate-based authentication for vehicle accident detection and notification in intelligent transportation systems. IEEE Sensors Journal, 1–1.

    Google Scholar 

  24. Vangala, A., Das, A. K., Kumar, N., & Alazab, M. (2020). Smart secure sensing for IoT-based agriculture: Blockchain perspective. IEEE Sensors Journal, 1–1.

    Google Scholar 

  25. Verma, G. K., Kumar, N., Gope, P., Singh, B. B., & Singh, H. (2021). Scbs: A short certificate-based signature scheme with efficient aggregation for industrial internet of things environment. IEEE Internet of Things Journal, 1–1.

    Google Scholar 

  26. Wen, Z., Garg, S., Aujla, G. S. S., Alwasel, K., Puthal, D., Dustdar, S., Zomaya, A. Y., & Rajan, R. (2020). Running industrial workflow applications in a software-defined multi-cloud environment using green energy aware scheduling algorithm. IEEE Transactions on Industrial Informatics, 1–1.

    Google Scholar 

  27. Zhang, P., Zhang, Z., & Zhang, W. (2013). An approach of semantic similarity by combining HowNet and Cilin. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing (pp. 1638–1643).

    Google Scholar 

  28. Zhang, P., Yao, H., & Liu, Y. (2016). Virtual network embedding based on the degree and clustering coefficient information. IEEE Access, 4, 8572–8580.

    Article  Google Scholar 

  29. Zhang, P., Wu, S., Wang, M., Yao, H., & Liu, Y. (2018). Topology based reliable virtual network embedding from a QoE perspective. China Communications, 15(10), 38–50.

    Article  Google Scholar 

  30. Zhang, P., Yao, H., & Liu, Y. (2018). Virtual network embedding based on computing, network, and storage resource constraints. IEEE Internet of Things Journal, 5(5), 3298–3304.

    Article  Google Scholar 

  31. Zhang, P., Huang, X., & Li, M. (2019). Disease prediction and early intervention system based on symptom similarity analysis. IEEE Access, 7, 176484–176494.

    Article  Google Scholar 

  32. Zhang, P., Hong, Y., Pang, X., & Jiang, C. (2020). VNE-HPSO: Virtual network embedding algorithm based on hybrid particle swarm optimization. IEEE Access, 8, 213389–213400.

    Article  Google Scholar 

  33. Zhang, P., Li, C., & Wang, C. (2020). Smarttext: Learning to generate harmonious textual layout over natural image. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1–6).

    Google Scholar 

  34. Zhang, P., Pang, X., Bi, Y., Yao, H., Pan, H., & Kumar, N. (2020). Dscd: Delay sensitive cross-domain virtual network embedding algorithm. IEEE Transactions on Network Science and Engineering, 7(4), 2913–2925.

    Article  Google Scholar 

  35. Zhang, P., Pang, X., Kumar, N., Aujla, G. S., & Cao, H. (2020). A reliable data-transmission mechanism using blockchain in edge computing scenarios. IEEE Internet of Things Journal, 1–1.

    Google Scholar 

  36. Zhang, P., Wang, C., Aujla, G. S., Kumar, N., & Guizani, M. (2020). IoV scenario: Implementation of a bandwidth aware algorithm in wireless network communication mode. IEEE Transactions on Vehicular Technology, 69(12), 15774–15785.

    Article  Google Scholar 

  37. Zhang, P., Wang, C., Aujla, G. S., & Pang, X. (2020). A node probability-based reinforcement learning framework for virtual network embedding. In 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM) (pp. 421–426).

    Google Scholar 

  38. Zhang, P., Wang, C., Jiang, C., & Benslimane, A. (2020). Security-aware virtual network embedding algorithm based on reinforcement learning. IEEE Transactions on Network Science and Engineering, 1–1.

    Google Scholar 

  39. Zhang, P., Huang, X., Wang, Y., Jiang, C., He, S., & Wang, H. (2021). Semantic similarity computing model based on multi model fine-grained nonlinear fusion. IEEE Access, 9, 8433–8443.

    Article  Google Scholar 

  40. Zhang, P., Jiang, C., Pang, X., & Qian, Y. (2021). Stec-IoT: A security tactic by virtualizing edge computing on IoT. IEEE Internet of Things Journal, 8(4), 2459–2467.

    Article  Google Scholar 

  41. Zhang, P., Li, C., & Wang, C. (2021). Viscode: Embedding information in visualization images using encoder-decoder network. IEEE Transactions on Visualization and Computer Graphics, 27(2), 326–336.

    Article  Google Scholar 

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Acknowledgements

This work is partially supported by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-006, partially supported by Shandong Provincial Natural Science Foundation under Grant ZR2020MF006, and partially supported by “the Fundamental Research Funds for the Central Universities” of China University of Petroleum (East China) under Grant 20CX05017A.

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Correspondence to Peiying Zhang .

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Liu, F., Kibalya, G., Santhosh Kumar, S.V.N., Zhang, P. (2022). Challenges of Traditional Networks and Development of Programmable Networks. In: Aujla, G.S., Garg, S., Kaur, K., Sikdar, B. (eds) Software Defined Internet of Everything. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-89328-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-89328-6_3

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