CBR-based network performance management with multi-agent approach

Nowadays, with the growth of heterogeneity of network equipment and software, and involved a variety of advanced network technology, the complexity of computer network have rapidly increased. Especially demands for high-performance network service in advanced data-intensive scientific research are dramatically increased. In order to cope with the network performance issue, this paper proposes the end-to-end (ETE) network performance management framework based on case-based reasoning with the case library and multi-agent integrated with perfSONAR as well as large-scale network flow monitoring. It provides a sophisticated framework for both network operator and user to systemically identify ETE network performance issues that are detected, diagnosed and recovered. The real cases are modeled and implemented into the casebase in the real experimental environment, a national research network of Korea. To verify the proposed framework, validation of the cases in the casebase is demonstrated.