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Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science., {"references":["Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, and Oscar M. Caicedo. A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities. Journal of Internet Services and Applications, 9(1):16, June 2018.","Amina Adadi and Mohammed Berrada. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6:52138– 52160, 2018.","Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, and Amit P. Sheth. Machine Learning for Internet of Things Data Analysis: A Survey. Digital Communications and Networks, 4(3):161–175, August 2018.","Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. Adaptive computation and machine learning. MIT Press, Cambridge, Mass., nachdr. edition, 1998.","German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter. Continual Lifelong Learning with Neural Networks: A Review. Neural Networks, 113:54–71, May 2019.","Agnar Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun., 7(1):39–59, March 1994.","Petra Perner. Case-Based Reasoning and the Statistical Challenges II. In Dr. Aleksandra Gruca, Tadeusz Czachórski, and Stanisław Kozielski, editors, Man-Machine Interactions 3, Advances in Intelligent Systems and Computing, pages 17–38, Cham, 2014. Springer International Publishing.","Ali Boukehila and Nora Taleb. Case-Based Approach to Detect Emergence. In Proceedings of the 2019 3rd International Conference on Big Data Research, pages 98–102, 2019.","Malik Jahan Khan, Hussain Hayat, and Irfan Awan. Hybrid Case-Base Maintenance Approach for Modeling Large Scale Case-Based Reasoning Systems. Human-centric Computing and Information Sciences, 9(1):9, March 2019.","Arkadiusz Weglarz and Paweł Gilewski. Application of CBR Systems in the Process of Energy Retrofit of Single-Family Detached Houses. MATEC Web of Conferences, 196:02033, 2018. Publisher: EDP Sciences.","Lukasz Osuszek and Stanisław Stanek. Case Based Reasoning as an Element of Case Processing in Adaptive Case Management Systems. Annals of Computer Science and Information Systems, 6:217–223, 2015.","Yikun Su, Shijing Yang, Kangning Liu, Kaicheng Hua, and Qi Yao. Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry. International Journal of Environmental Research and Public Health, 16(9), May 2019.","Nabanita Choudhury and Shahin Ara Begum. A Survey on Case-based Reasoning in Medicine. In Proceedings of the International Journal of Advanced Computer Science and Applications, volume 7 of 8, pages 136–144, 2016.","Hugo Lopez-Fernandez, Florentino Fernandez Riverola, Miguel Reboiro-Jato, Daniel Glez-Pena, and Jose R. Mendez. Using CBR as Design Methodology for Developing Adaptable Decision Support Systems. Efficient Decision Support Systems - Practice and Challenges From Current to Future, 2011.","Flávio G. Calhau and Joberto S B Martins. A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid. In Proceedings of the VIII Brazilian Conference on Intelligent Systems (BRACIS), pages 1–6, Salvador, Brazil, October 2019.","Eliseu M. Oliveira, Rafael F. Reale, and Joberto S. B. Martins. Evaluating CBR Similarity Functions for BAM Switching in Networks with Dynamic Traffic Profile. In Proceedings of the V International Workshop on ADVANCEs in ICT Infrastructure and Services, pages 1–7, Paris, January 2017.","Aldo Von Wangenheim, Christiane Gresse von Wangenheim, and Thiago Rateke. Raciocínio Baseado em Casos - 2ª ed. Revisada e Atualizada. 07 2013.","I. Watson. Case-Based Reasoning Is a Methodology Not a Technology. Knowledge-Based Systems, 12(5):303–308, October 1999.","Zhaoyu Zhai, José-Fernán Martínez Ortega, Victoria Beltran, and Néstor Lucas Martínez. An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval. Sensors, 19(23):5118, January 2019.","Ralph Bergmann and Klaus-Dieter Althoff. Methodology for Building CBR Applications. Case-Based Reasoning Technology, pages 299–326, 1998.","Rosina Weber, Jason M. Proctor, Ilya Waldstein, and Andres Kriete. CBR for Modeling Complex Systems. In Case-Based Reasoning Research and Development, pages 625–639, August 2005.","Jean-Baptiste Lamy, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, and Brigitte Séroussi. Explainable Artificial Intelligence for Breast Cancer: A Visual Case-Based Reasoning Approach. Artificial Intelligence in Medicine, 94:42–53, March 2019.","Kuo-Sui Lin. A Case-Based Reasoning System for Interior Design Using a New Cosine Similarity Retrieval Algorithm. Journal of Information and Telecommunication, 4(1):91–104, January 2020.","Nahyun Kwon, Kwonsik Song, Moonseo Park, Youjin Jang, Inseok Yoon, and Yonghan Ahn. Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm. Sustainability, 11(11):3074, January 2019.","Rafael Freitas Reale, Walter da Costa Pinto Neto, and Joberto S. B. Martins. AllocTC-Sharing: A New Bandwidth Allocation Model for DS-TE Networks. In Proceedings of the VII Latin American Network Operations and Management Symposium, pages 1–4, Quito, Equador, October 2011.","Joberto Martins, Romildo Bezerra, Rafael Reale, and Gilvan Durães. Uma Visão Tutorial dos Modelos de Alocação de Banda como Mecanismo de Provisionamento de Recursos em Redes IP/MPLS. Revista de Sistemas e Computação, 5(2):144–155, December 2015.","Eliseu M. Oliveira, Rafael F. Reale, and Joberto S. B. Martins. Cognitive Management of Bandwidth Allocation Models with Case-Based Reasoning - Evidences Towards Dynamic BAM Reconfiguration. In Proceedings of the IEEE International Symposium on Computers and Communications (ISCC), pages 397–493, Brazil, June 2018.","Rafael Reale, Romildo Bezerra, and Joberto Martins. A Preliminary Evaluation of Bandwidth Allocation Model Dynamic Switching. International Journal of Computer Networks and Communications, 6(3):131–143, May 2014."]}

info:eu-repo/semantics/publishedVersion

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info:eu-repo/semantics/publishedVersion

Management is a complex task in today’s heterogeneous and large scale networks like Cloud, Internet of Things (IoT) , vehicular and Multiprotocol Label Switching (MPLS) networks. Likewise, researchers and developers envision the use of artificial intelligence techniques to create cognitive and autonomic management tools that aim better assist and enhance the management process cycle. Bandwidth Allocation Models (BAMs) are a resource allocation solution for networks that need to share and optimize limited resources like bandwidth, fiber or optical slots in a flexible and dynamic way. This paper proposes and evaluates the use of Case-based Reasoning (CBR) for the cognitive management of BAM reconfiguration in MPLS networks. The results suggest that CBR learns about bandwidth request profiles associated with the current network state and is able to dynamically define or assist in BAM reconfiguration. The BAM reconfiguration approach adopted is based on switching among available BAM implementations (Maximum Allocation Model, Russian Dolls Model and AllocTC-Sharing). The cognitive management proposed allows BAMs self-configuration and results in optimizing the utilization of network resources., {"references":["H. Elsawy, H. Dahrouj, T. Y. Al-naffouri, and M. s. Alouini, \"Virtualized cognitive network architecture for 5g cellular networks,\" IEEE Communications Magazine, vol. 53, no. 7, pp. 78–85, Jul. 2015.","Q. H. Mahmoud, Cognitive Networks Towards Self-Aware Networks. Hoboken, NJ: John Wiley & Sons, 2007, oCLC: 839295888.","O. Rendon, F. Estrada-Solano, R. Boutaba, N. Shahriar, M. Salahuddin, N. Liman, and S. Ayoubi, \"Machine Learning for Cognitive Network Management,\" IEEE Communications Magazine, pp. 1–9, 2018.","L. Song, D. Niyato, Z. Han, and E. Hossain, \"Game-theoretic resource allocation methods for device-to-device communication,\" IEEE Wireless Communications, vol. 21, no. 3, pp. 136–144, Jun. 2014.","S. Singh and I. Chana, \"QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review,\" ACM Comput. Surv., vol. 48, no. 3, pp. 42:1–42:46, Dec. 2015.","R. Reale, R. Bezerra, and J. Martins, \"A Preliminary Evaluation of Bandwidth Allocation Model Dynamic Switching,\" Int. Jour. of Computer Networks & Communications, vol. 6, no. 3, pp. 131–143, May 2014","J. Hui, \"Resource Allocation for Broadband Networks,\" IEEE Jour. on Selec. Areas in Commun., vol. 6, no. 9, pp. 1598–1608, Dec. 1988.","L. Xiao, M. Johansson, and S. P. Boyd, \"Simultaneous Routing and Resource Allocation Via Dual Decomposition,\" IEEE Transactions on Communications, vol. 52, no. 7, pp. 1136–1144, Jul. 2004.","M. Guizani, B. Khalfi, M. B. Ghorbel, and B. Hamdaoui, \"Largescale cognitive cellular systems: Resource management overview,\" IEEE Communications Magazine, vol. 53, no. 5, pp. 44–51, May 2015.","N. Cordeschi, D. Amendola, and E. Baccarelli, \"Reliable Adaptive Resource Management for Cognitive Cloud Vehicular Networks,\" IEEE Trans. on Veh. Technology, vol. 64, no. 6, pp. 2528–2537, Jun. 2015.","E. Lagunas, S. K. Sharma, S. Maleki, S. Chatzinotas, and B. Ottersten, \"Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks,\" IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 305–317, Sep. 2015.","H. Zhang, C. Jiang, N. C. Beaulieu, X. Chu, X. Wang, and T. Q. S. Quek, \"Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach,\" IEEE Transactions on Wireless Communications, vol. 14, no. 6, pp. 3481–3493, Jun. 2015.","C. Wu, K. Chowdhury, M. Di Felice, and W. Meleis, \"Spectrum Management of Cognitive Radio Using Multi-Agent Reinforcement Learning,\" in Proc. of the 9th Int. Conf. on Autonomous Agents and Multiagent Systems, May 2010, pp. 1705–1712","F. L. Faucher and W. Lai, \"Maximum Allocation Bandwidth Constraints Model for DiffServ-aware MPLS Traffic Engineering,\" Internet Engineering Task Force, Request for Comments RFC 4125, Jun. 2005.","W. da Costa Pinto Neto and J. S. B. Martins, \"A RDM-like bandwidth management algorithm for Traffic Engineering with DiffServ and MPLS support.\" St. Petersburg, Russia: IEEE, Jun. 2008, pp. 1–6.","R. Reale, W. Neto, and J. Martins, \"AllocTC-sharing: A New Bandwidth Allocation Model for DS-TE Networks,\" in Proc. of the 7th Latin American Network Oper. and Manag. Symp., Oct. 2011, pp. 1–4.","R. Reale, R. Martins da Silva Bezerra, and J. Martins, \"G-BAM: A Generalized Bandwidth Allocation Model for IP/MPLS/DS-TE Networks,\" International Journal of Computer Information Systems and Industrial Management Applications, vol. 6, pp. 635–643, Dec. 2014.","M. Pistek, M. Medvecky, and S. Klucik, \"A-MAR: A New Bandwidth Constraint Model for DS-TE Networks,\" in 38th Int. Conference on Telecommunications and Signal Processing, Jul. 2015, pp. 1–5.","W. da Costa P. Neto and J. S. B. Martins, \"Adapt-RDM - A Bandwidth Management Algorithm suitable for DiffServ Services Aware Traffic Engineering.\" IEEE, 2008, pp. 975–978.","J. S. B. Martins, \"RePAF Project,\" JSMNet Technical Report Vol 18, No 1, 2017.","C. Tata and M. Kadoch, \"CAM: Courteous bandwidth constraints allocation model,\" in Proceedings of the 20th International Conference on Telecommunications - ICT 2013. IEEE, May 2013, pp. 1–5.","M. Gu and A. Aamodt, \"Evaluating CBR Systems Using Different Data Sources: A Case Study,\" in Advances in Case-Based Reasoning, ser. LNCS. Springer, Berlin, Heidelberg, Sep. 2006, pp. 121–135.","P. R. CohN. Agoulmine, Ed., Autonomic Network Management Principles: From Concepts to Applications. Amsterdam: Elsevier, 2011.en and A. E. Howe, \"Toward AI Research Methodology: Three Case Studies in Evaluation,\" IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 3, pp. 634–646, May 1989.","B. Jennings, S. V. D. Meer, S. Balasubramaniam, and D. Botvich, \"Towards Autonomic Management of Communications Networks,\" IEEE Communications Magazine, vol. 45, no. 10, pp. 112–121, Oct. 2007.","A. Aamodt and E. Plaza, \"Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches,\" Artificial Intelligence Communications, vol. 7, no. 1, pp. 39–59, 1994.","E. Oliveira, R. Reale, and J. Martins, \"Evaluating CBR Similarity Functions for BAM Switching in Networks with Dynamic Traffic Profile,\" in 5th Int. Workshop on ADVANCEs in ICT Infrastructure and Services, Paris, Jan. 2017, pp. 1–7.","J. A. Recio-Garc´ıa, P. A. Gonz´alez-Calero, and B. D´ıaz-Agudo, \"J Colibri2: A Framework for Building Case-Based Reasoning Systems,\" Science of Computer Programming, vol. 79, pp. 126–145, Jan. 2014."]}

This "ILLUSTRATED TECHNICAL PAPER" presents the slides of the paper "Cognitive Management of Bandwidth Allocation Models with Case-based Reasoning - Evidences Towards Dynamic BAM Reconfiguration". The talk was presented at IEEE Symposium on Computers and Communications - ISCC 2018, 25 - 28 June 2018 at Natal, Brazil. The "illustrated technical paper format" format is used with slides, complementary text and bibliographic references to subsidize and enrich paper contents lecture., {"references":["H. Elsawy, H. Dahrouj, T. Y. Al-Naffouri, and M. s Alouini. Virtualized cognitive network architecture for 5G cellular networks. IEEE Communications Magazine, 53(7):78–85, July 2015.","Qusay H Mahmoud. Cognitive Networks Towards Self-Aware Networks. John Wiley Sons, Hoboken, NJ, 2007.","Oscar Rendon, Felipe Estrada-Solano, Raouf Boutaba, Nashid Shahriar, Mohammad Salahuddin, Noura Liman, and SaraAyoubi. Machine Learning for Cognitive Network Management.IEEE Communications Magazine, pages 1–9, 2018.","L. Song, D. Niyato, Z. Han, and E. Hossain, \"Game-theoretic resource allocation methods for device-to-device communication,\" IEEE Wireless Communications, vol. 21, no. 3, pp. 136–144, Jun. 2014.","S. Singh and I. Chana, \"QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review,\" ACM Comput. Surv., vol. 48, no. 3, pp. 42:1–42:46, Dec. 2015.","R. Reale, R. Bezerra, and J. Martins, \"A Preliminary Evaluation of Bandwidth Allocation Model Dynamic Switching,\" Int. Jour. of Computer Networks Communications, vol. 6, no. 3, pp. 131–143, May 2014.","J. Hui, \"Resource Allocation for Broadband Networks,\" IEEE Jour. on Selec. Areas in Commun., vol. 6, no. 9, pp. 1598–1608, Dec. 1988.","L. Xiao, M. Johansson, and S. P. Boyd, \"Simultaneous Routing and Resource Allocation Via Dual Decomposition,\" IEEE Transactions on Communications, vol. 52, no. 7, pp. 1136–1144, Jul. 2004.","M. Guizani, B. Khalfi, M. B. Ghorbel, and B. Hamdaoui, \"Largescale cognitive cellular systems: Resource management overview,\" IEEE Communications Magazine, vol. 53, no. 5, pp. 44–51, May 2015","N. Cordeschi, D. Amendola, and E. Baccarelli, \"Reliable Adaptive Resource Management for Cognitive Cloud Vehicular Networks,\" IEEE Trans. on Veh. Technology, vol. 64, no. 6, pp. 2528–2537, Jun. 2015.","E. Lagunas, S. K. Sharma, S. Maleki, S. Chatzinotas, and B. Ottersten, \"Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks,\" IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 305–317, Sep. 2015.","H. Zhang, C. Jiang, N. C. Beaulieu, X. Chu, X. Wang, and T. Q. S. Quek, \"Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach,\" IEEE Transactions on Wireless Communications, vol. 14, no. 6, pp. 3481–3493, Jun. 2015.","C. Wu, K. Chowdhury, M. Di Felice, and W. Meleis, \"Spectrum Management of Cognitive Radio Using Multi-Agent Reinforcement Learning,\" in Proc. of the 9th Int. Conf. on Autonomous Agents and Multiagent Systems, May 2010, pp. 1705–1712.","F. L. Faucher andW. Lai, \"Maximum Allocation Bandwidth Constraints Model for DiffServ-aware MPLS Traffic Engineering,\" Internet Engineering Task Force, Request for Comments RFC 4125, Jun. 2005.","W. da Costa Pinto Neto and J. S. B. Martins, \"A RDM-like bandwidth management algorithm for Traffic Engineering with DiffServ and MPLS support.\" St. Petersburg, Russia: IEEE, Jun. 2008, pp. 1–6.","R. Reale, W. Neto, and J. Martins, \"AllocTC-sharing: A New Bandwidth Allocation Model for DS-TE Networks,\" in Proc. of the 7th Latin American Network Oper. and Manag. Symp., Oct. 2011, pp. 1–4.","R. Reale, R. Martins da Silva Bezerra, and J. Martins, \"G-BAM: A Generalized Bandwidth Allocation Model for IP/MPLS/DS-TE Networks,\" International Journal of Computer Information Systems and Industrial Management Applications, vol. 6, pp. 635–643, Dec. 2014.","M. Pistek, M. Medvecky, and S. Klucik, \"A-MAR: A New Bandwidth Constraint Model for DS-TE Networks,\" in 38th Int. Conference on Telecommunications and Signal Processing, Jul. 2015, pp. 1–5.","W. da Costa P. Neto and J. S. B. Martins, \"Adapt-RDM - A Bandwidth Management Algorithm suitable for DiffServ Services Aware Traffic Engineering.\" IEEE, 2008, pp. 975–978.","J. S. B. Martins, \"RePAF Project,\" JSMNet Technical Report Vol 18, No 1, 2017.","C. Tata and M. Kadoch, \"CAM: Courteous bandwidth constraints allocation model,\" in Proceedings of the 20th International Conference on Telecommunications - ICT 2013. IEEE, May 2013, pp. 1–5.","M. Gu and A. Aamodt, \"Evaluating CBR Systems Using Different Data Sources: A Case Study,\" in Advances in Case-Based Reasoning, ser. LNCS. Springer, Berlin, Heidelberg, Sep. 2006, pp. 121–135.","P. R. Cohen and A. E. Howe, \"Toward AI Research Methodology: Three Case Studies in Evaluation,\" IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 3, pp. 634–646, May 1989.","N. Agoulmine, Ed., Autonomic Network Management Principles: From Concepts to Applications. Amsterdam: Elsevier, 2011.","B. Jennings, S. V. D. Meer, S. Balasubramaniam, and D. Botvich, \"Towards Autonomic Management of Communications Networks,\" IEEE Communications Magazine, vol. 45, no. 10, pp. 112–121, Oct. 2007.","A. Aamodt and E. Plaza, \"Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches,\" Artificial Intelligence Communications, vol. 7, no. 1, pp. 39–59, 1994.","E. Oliveira, R. Reale, and J. Martins, \"Evaluating CBR Similarity Functions for BAM Switching in Networks with Dynamic Traffic Profile,\" in 5th Int. Workshop on ADVANCEs in ICT Infrastructure and Services, Paris, Jan. 2017, pp. 1–7.","J. A. Recio-Garcia, P. A. Gonzalez-Calero, and B. Dıaz-Agudo, \"J Colibri2: A Framework for Building Case-Based Reasoning Systems,\" Science of Computer Programming, vol. 79, pp. 126–145, Jan. 2014."]}

info:eu-repo/semantics/publishedVersion

info:eu-repo/semantics/publishedVersion

info:eu-repo/semantics/publishedVersion

info:eu-repo/semantics/publishedVersion

info:eu-repo/semantics/acceptedVersion