The paper “ Key Issues to Be Considered while Dealing with Artificial Intelligence Fault Management” is an apposite example of a literature review on information technology. Intelligent fault management continues to grow and this means that approaches to network management have to be looked into from different perspectives. This development has been complicated by the fact that computer networks are continuously growing in complexity and size. For instance, at any given time, issues such as enterprise networks will consist of different hardware platforms or architectures, user interface technologies, and communication protocol making the need for intelligent fault management essential.
On the other hand, performance requirements and high availability have placed a high demand on proper management of such networks. There is currently a need to increase efficiency which has often resulted in the decline in the ration between the network elements and network operators that are managed. This has further caused a loss in service and network availability. In such cases, it has been essential to integrate or bring together an expert system into the network management architecture so as to transform events of the network into value-added information that enables the operators of the network to properly manage the network.
Based on this information, this paper presents a critical assessment of intelligent fault management. IntroductionAs studies have noted, there have been traditional network management activities like fault management that need different approaches (Varga, Jennings & Cockburn 1994). These managements have been performed with the involvement of human actions. However, as Sterritt et al. (2000) also note, these are activities that are becoming more contentious and demanding and data-intensive, as a result of the heterogeneous ways of and increasing the size of networks currently seen.
It is for this reason that a need for artificial intelligence is required in such management. Korbicz et al. (2012) argue that artificial intelligence will play a significant role in the process of solving problems as well as reasoning techniques that are used in the process of fault management. Just like Ramchurn et al. (2012) noted, expert systems have been reported to have been successfully applied to some types of fault management. However, there has been a number of concerns that have been raised.
First, enterprise computer networks are rapidly increasing in size and complexity. A typical enterprise network consists of numerous hardware architectures, communication protocols, and user interface technologies. High availability and performance requirements place a high demand on the management of such networks. The need to increase efficiency often results in the decline in the ratio between the network operators and the network elements being managed, resulting in a loss of network availability and service. In such scenarios, it is essential to integrate an expert system into the network management architecture to transform network events into value-added information enabling the network operators to efficiently manage the network.
In this paper, we present details of an intelligent network management system that is successfully being used to manage a large Internet service provider network. Secondly, these systems have been found not to be flexible enough with regard to today’ s evolving needs in the network (Strasser et al. 2015). It is therefore because of this reason that there is a need to propose an Artificial Intelligence (AI) solution that applies both the neural networks and case-based techniques of reasoning for the management of faults for networks.
Corn, P.A., Dube, R., McMichael, A.F., & Tsay, J.L. (1988) An autonomous distributed expert system for switched network maintenance. In Proceedings of IEEE GLOBECOM’88 (pp. 1530-1537).
Ferreira, V. H., Zanghi, R., Fortes, M. Z., Sotelo, G. G., Silva, R. B. M., Souza, J. C. S., ... & Gomes, S. (2016). A survey on intelligent system application to fault diagnosis in electric power system transmission lines. Electric Power Systems Research, 136, 135-153.
Joseph, C., Kindrick, J. Muralidhar, K. So, C. & Toth-Fejel, T. (1989) MAP fault management expert system. In Meandzija, B. & Westcott, J. (Eds.) Integrated Network Management, I. North-Holland: Elsevier Science Publishers B.V.
Korbicz, J., Koscielny, J. M., Kowalczuk, Z., & Cholewa, W. (Eds.). (2012). Fault diagnosis: models, artificial intelligence, applications. Springer Science & Business Media.
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012). Putting the'smarts' into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM, 55(4), 86-97.
Sterritt, R., Marshall, A. H., Shapcott, C. M., & McClean, S. I. (2000). Exploring dynamic Bayesian belief networks for intelligent fault management systems. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on (Vol. 5, pp. 3646-3652). IEEE.
Strasser, T., Andren, F., Kathan, J., Ceci, C., Buccella, C., Siano, P., ... & Marík, V. (2015). A review of architectures and concepts for intelligence in future electric energy systems. Industrial Electronics, IEEE Transactions on, 62(4), 2424-2438.
Travé-Massuyès, L. (2014)"Bridging control and artificial intelligence theories for diagnosis: A survey." Engineering Applications of Artificial Intelligence 27: 1-16.
Varga, L., Jennings, N. R., & Cockburn, D. (1994). Integrating intelligent systems into a cooperating community for electricity distribution management. Expert Systems with Applications, 7(4), 563-579.
Yamahira, T., Kiriha, Y. & Sakata, S. (1989) Unified fault management scheme for network troubleshooting expert system. In Meandzija, B. & Westcott, J. (Eds.) Integrated Network Management, I. North-Holland: Elsevier Science Publishers B.V