Enterprise network topology discovery based on end-to-end metrics : Logical site discovery in enterprise networks based on application level measurements in peer- to-peer systems

Abstract: In data intensive applications deployed in enterprise networks, especially applications utilizing peer-to-peer technology, locality is of high importance. Peers should aim to maximize data exchange with other peers where the connectivity is the best. In order to achieve this, locality information must be present which peers can base their decisions on. This information is not trivial to find as there is no readily available global knowledge of which nodes have good connectivity. Having each peer try other peers randomly until it finds good enough partners is costly and lowers the locality of the application until it converges. In this thesis a solution is presented which creates a logical topology of a peer-to-peer network, grouping peers into clusters based on their connectivity metrics. This can then be used to aid the peer-to-peer partner selection algorithm to allow for intelligent partner selection. A graph model of the system is created, where peers in the system are modelled as vertices and connections between peers are modelled as edges, with a weight in relation to the quality of the connection. The problem is then modelled as a weighted graph clustering problem which is a well-researched problem with a lot of published work tied to it. State-of-the-art graph community detection algorithms are researched, selected depending on factors such as performance and scalability, optimized for the current purpose and implemented. The results of running the algorithms on the streaming data are evaluated against known information. The results show that unsupervised graph community detection algorithm creates useful insights into networks connectivity structure and can be used in peer-to-peer contexts to find the best partners to exchange data with.