COMPUTER NETWORKS GROUP - NETGROUP
Big Data Performance Monitoring of IP Networks
Thesis in external company
Research Groups COMPUTER NETWORKS GROUP - NETGROUP
Thesis type EXPEIRMENTAL, IN COMPANY
Description Performance monitoring of IP networks is of great importance for service providers because it allows them to manage Service Level Agreements (SLA) with internal and external customers.
In this context, one of the problems that are being addressed is the scalability of passive monitoring systems: if, for example, we want to monitor thousands of flows, it is necessary that each one of them is filtered by a specific capture rule loaded into each monitoring point. The problem that arises is that network devices support only a limited number of such rules, thus limiting the number of flows that can be monitored.
In order to solve this issue, it is possible to introduce the concept of multipoint flow, which groups together a set of elementary flows. Then, a smaller number of multipoint flows is monitored, instead of all the elementary flows of interest.
The data so captured, can then be processed in order to extract more precise information, e.g. referred to single elementary flows or certain subsets of elementary flows.
From a theoretical point of view, the way of defining multipoint flows and the above mentioned post-processing operations have been defined in a patent held by Telecom Italia.
The goal of the thesis, to be developed at Telecom Italia, is to implement them in a real system, where probes can be installed in the network, and the data they collect are stored in a DB and then post-processed by a software that can extract the information of interest for the administrator. The system will be based on already existing components (e.g. probes), while the DB and the post-processing software will be developed as part of the thesis work. The main challenges to be addressed regard the size of monitored data, which can be very large (big data).
Required skills IP Networks, Network Programming, Database, Interaction with remote DB
Deadline 11/09/2019 PROPONI LA TUA CANDIDATURA