Tavernier, Wouter

English

Ghent University Academic Bibliography

A traffic-driven IP router update process usingmachine learning techniquesWouter TavernierDepartment of Information Technology, Ghent University, IBCN,IBBTSupervisor(s): Mario Pickavet, Didier ColleThe task of a communication network is to transport packets from source nodes towards des-tination nodes. At a given point in time many nodes (or routers) receive and forward an almostcontinuous stream of packets. The behavior of such a router is determined by its forwarding table.This table marks for every destination in the network which outgoing link to take to reach its desti-nation. Because communication networks are not infallable, node or link failure occur. Upon suchan event, the router node adjacent to the failure cannot any longer use the failed link for reachingthe next hop. When a router detects such a failure, a reconfiguration process of the forwardingtable is initiated. This affects all packets towards destinations which would be using the failed link.In a typical backbone router of an Internet Service Provider (ISP), the reconfiguration can takehundreds of milliseconds or even more than 1 second. As long as an entry in the forwarding tableis configured to use a failed link, all network packets sent towards that link are lost.Because the reconfiguration happens in sets of entries corresponding to specific destinations,cleverly re-ordering these batches could significantly reduce the resulting packet loss. Indeed,ensuring that high traffic volume traffic volume would be reconfigured earlier than lower volumetraffic, is the goal of the performed research.The reordering of forwarding entries is more difficult than it appears at first sight. While oversufficiently long periods in time (tens of minutes) the relative proportions of network traffic towardsdifferent destinations might be quite stable, this is not the case for periods less than a second. There-fore, we evaluate prediction methods to make predictions of network traffic in the very near future.In the performed research, both machine learning techniques such as Neural Networks, SupportVector Machines, as well as time series prediction techniques such (F)AR(I)MA(-GARCH) wereevaluated.—————————————————–This work is supported by the European Commission (EC) Seventh Framework Programme (FP7) ECODEproject (Grant nr. 223936).

A traffic-driven IP router update process using machine learning techniques

https://biblio.ugent.be/publication/1967008/file/2952387.pdf

A traffic-driven IP router update process using machine learning techniques

Abstract

Similar works

Full text

Available Versions

Ghent University Academic Bibliography