Research on Active Detection Method of Network Congestion.

The rapid development of communication technology improves the feasibility of modern communication networks, such as wireless networks and satellite communication networks, etc. As a result, the complexity of networks increases significantly in terms of topology, increasing the difficulty of network resource allocation (i.e., network flow scheduling) and causing network congestion. It is worth noting that congestion is extremely destructive to the network, causing the network's overall performance to decline and even crashes and paralysis in severe cases. Therefore, timely identification and dealing with the network congestion are hot and difficult points of current research. Currently, most works (i.e., the active and passive measurement) identify the congestion on the node level, which may cause impractical issues with the increasing number of nodes (i.e., increasing detection probes will exacerbate the network congestion). To this end, this paper innovatively develops an active detection method based on the improved ant colony algorithm (IAC) for congestion identification on the path level. The developed method uses the IAC algorithm to find the optimal path and identifies the congested path by comparing the optimal path before and after congestion. Besides, the queuing theory model is utilized to obtain the performance metrics (i.e., queuing waiting time) to evaluate the optimal path. Experimental results show that the developed method can accurately identify the congested path in the network. This method makes identifying congestion less expensive and has higher operational feasibility, which is meaningful for the practical reference.