Simulation & Analysis of Peer-to-Peer Network Quality for Measurement Scheduling : Online algorithms, Application for Network QoS Monitoring

Abstract: With the growing dependency on Internet connectivity in our daily lives, monitoring connection quality to ensure a good quality of service has become increasingly important. The CheesePi project aims to build a platform for monitoring connection quality from the home user’s perspective. And with peer to peer technologies becoming more prevalent the need for quality of service monitoring between peers become more important. This thesis analyses the problem of scheduling connection quality measurements between peers in a network. A method is presented for scheduling measurements which make use of statistical models of the individual links in the network based on previous measurement data. The method applies the ADWIN1 adaptive windowing algorithm over the models and decides a priority based on the relative window sizes for each link. This method is evaluated against a round-robin scheduler through simulation and is shown to provide a better scheduling than round-robin in most cases in terms of achieving the most “information gain” per measurement iteration. The results show that for sudden changes in a network link the scheduler prioritises measurements for that link and therefore converge its view of the network to the new stable state more quickly than when using round-robin scheduling. The scheduling method was developed to be practically applicable to the CheesePi project and might effectively be deployed in real systems running the CheesePi platform. The thesis also contains an evaluation of two online algorithms for mean and variance as to how they react to change in the data source from which the samples are taken.