Integrating Context Awareness and SDN for a Lightweight Approach to Adaptive Networking

• José C. F. M. Júnior Instituto de Tecnologia de Pernambuco – ITEP / UFPE
• David C. P. da Cunha UFPE
• Carlos A. G. Ferraz UFPE https://orcid.org/0000-0002-4861-0719

DOI: https://doi.org/10.5753/sbcup.2021.16007

Resumo

With computer networks everywhere, managing them is becoming increasingly complex, leading to higher costs, longer response times, and more human errors in decision making. Given that, the search for a Smart Network Management, with less human intervention and more automation, becomes indispensable. Several technologies have been proposed to enable “Smart Management,” including machine learning, notably for more complex cases. The Software-Defined Networking (SDN) paradigm is one of the most promising. Literature shows, however, that there are still several research challenges and opportunities for the automation of many network management tasks. This paper proposes to combine context awareness with SDN in a lightweight approach to Smart Management, or Adaptive Networking, to handle in a simplified way everyday events such as link-down and traffic congestion. A comparative analysis shows that the management approach combining context awareness with SDN is between 40% and 73% faster than the support of SDN to a human-driven management.

Palavras-chave: Context Awareness, SDN, Lightweight Approach, Adaptive Networking

Referências

Ayoubi, S., Limam, N., Salahuddin, M. A., Shahriar, N., Boutaba, R., Estrada-Solano, F., and Caicedo, O. M. (2018). Machine learning for cognitive network management. IEEE Communications Magazine, 56(1):158–165.

Bui, N., Cesana, M., Hosseini, S. A., Liao, Q., Malanchini, I., and Widmer, J. (2017). A survey of anticipatory mobile networking: Context-based classification, prediction methodologies, and optimization techniques. IEEE Communications Surveys Tutorials, 19(3):1790–1821.

e Silva, D. V. (2016). CD-CARS: Cross-Domain Context-Aware Recommender Systems. PhD thesis, Universidade Federal de Pernambuco. Ciências da Computação.

Hadjiantonis, A. M. (2012). Autonomic management of mobile and wireless networks. In Telecommunication Economics, pages 199–208. Springer, Berlin, Heidelberg.

Khan, M. A., Peters, S., Sahinel, D., Pozo-Pardo, F. D., and Dang, X.-T. (2018). Understanding autonomic network management: A look into the past, a solution for the future. Computer Communications, 122:93 – 117.

Kim, H. and Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51(2):114–119.

Koley, B. (2016). The zero touch network. 2016 IEEE 12th International Conference on Network and Service Management (CNSM).

Larson, R. and Farber, B. (2010). Estat ́ıstica Aplicada. Pearson Prentice Hall, São Paulo, Brasil, 4 edition.

Lee, Y., Vilalta, R., Casellas, R., Martínez, R., and Muñoz, R. (2018). Auto-scaling mechanism in the ict converged cross stratum orchestration architecture for zero-touch service and network management. In 2018 20th International Conference on Trans- parent Optical Networks (ICTON), pages 1–4.

Liu, J. and Xu, Q. (2019). Machine Learning in Software Defined Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), (Itnec):1114–1120.

Megyesi, P., Botta, A., Aceto, G., Pescapé, A., and Molnár, S. (2017). Challenges and solution for measuring available bandwidth in software defined networks. Computer Communications, 99:48–61.

Nicolaou, C. (1990). An architecture for real-time multimedia communication systems. IEEE Journal on Selected Areas in Communications, 8(3):391–400.

Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., and Turletti, T. (2014). A survey of software-defined networking. past, present, and future of programmable networks. IEEE Communications Surveys Tutorials, 16(3):1617–1634.

OPEN NETWORKING FOUNDATION (2015). OpenFlow Switch Specification 1.5.1.

Rojas, E. (2018). From software-defined to human-defined networking: Challenges and opportunities. IEEE Network, 32(1):179–185.

Shirmarz, A. and Ghaffari, A. (2020). Performance issues and solutions in sdn-based data center: a survey. The Journal of Supercomputing, 76(10):7545–7593.

Shu, Z., Wan, J., Lin, J., Wang, S., Li, D., Rho, S., and Yang, C. (2016). Traffic engineering in software-defined networking: Measurement and management. IEEE Access, 4:3246–3256.

Van Rossem, S., Cai, X., Cerratoz, I., Danielsson, P., Németh, F., Pechenot, B., Pelle, I., Risso, F., Sharma, S., Sköldström, P., and John, W. (2017). NFV service dynamicity with a DevOps approach. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pages 865–866.