Knowledge-Based Network Management System for Movable and Deployable ICT Resource Unit

When a disaster occurs, the demand for information and communication technology (ICT) services drastically increases. To meet such demands, a national project was undertaken in Japan to develop the Movable and Deployable ICT Resource Unit (MDRU). One challenge regarding the MDRU is securing operators to work the units in emergency situations. As ICT service users have diverse and frequently changing demands, strong technical skills and practical knowledge are required for the administration of MDRUs. In this paper, we propose a knowledge-based network management system to alleviate the burden on administrators. To deal with the structural changes to network systems that frequently occur with changes in ICT service demand, we introduce modularization techniques into our previous research. The proposed system can be easily reconfigured by join/disjoin modules corresponding to changes in the system configuration of the MDRU. The results of our experiments using the implemented experimental system confirm that the proposed system can be applied to MDRU operation and effectively supports administrators.

[1]  Fumiyuki Adachi,et al.  Disaster-resilient networking: a new vision based on movable and deployable resource units , 2013, IEEE Network.

[2]  Tetsuo Kinoshita,et al.  A Practical Design and Implementation of Active Information Resource based Network Management System , 2011 .

[3]  R.N. Cronk,et al.  Rule-based expert systems for network management and operations: an introduction , 1988, IEEE Network.

[4]  Feng Xia,et al.  A survey on virtual machine migration and server consolidation frameworks for cloud data centers , 2015, J. Netw. Comput. Appl..

[5]  Gabriel-Miro Muntean,et al.  A recommender system architecture for predictive telecom network management , 2015, IEEE Communications Magazine.

[6]  Mitsuyoshi Kobayashi,et al.  Experience of infrastructure damage caused by the Great East Japan Earthquake and countermeasures against future disasters , 2014, IEEE Communications Magazine.

[7]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[8]  Cosimo Anglano,et al.  Case-Based Reasoning for Autonomous Service Failure Diagnosis and Remediation in Software Systems , 2006, ECCBR.

[9]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[10]  M. J. Ross,et al.  An AI-based network management system , 1988, Seventh Annual International Phoenix Conference on Computers an Communications. 1988 Conference Proceedings.

[11]  Tetsuo Kinoshita Agent-Based Active Information Resource and Its Applications , 2010, DNIS.

[12]  Guy Pujolle,et al.  A Survey of Autonomic Network Architectures and Evaluation Criteria , 2012, IEEE Communications Surveys & Tutorials.

[13]  Mohsen Guizani,et al.  Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers , 2015, IEEE Transactions on Network and Service Management.

[14]  Tetsuo Kinoshita,et al.  A knowledge-based support method for autonomous service operations after disasters , 2013, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

[15]  Kenji Sugawara,et al.  Repository-Based Multiagent Framework for Developing Agent Systems , 2011 .