Cooperative learning for online in-network performance monitoring

Abstract

Motivated by the principles of decentralized in-network management (INM) for future networks, we consider the issue of information exchange among network nodes to improve network performance and scalability. INM concept gives autonomy to each network node to self-govern its behavior and participate in a distributed management in collaboration with the nodes to analyze and manage network resources. However, to ensure this interaction, exchange of network information is imperative. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of around 6% on both Cambridge and UNIBS datasets compared to nodes without cooperative learning capability.