An Analysis on End-To-End Inference Methods based On Packet Probing in Network

Current Internet is a massive, distributed network which continues to grow in size as globalization takes major role in everyone’s life like e-commerce, social networking and related activities grow. The heterogeneous and largely unregulated structure of the Internet renders tasks such as optimized service provision, rate limiting certain classes of applications (e.g. peer-to-peer), provide bandwidth guarantee for certain applications, avoiding shared congestion in flows are increasingly challenging tasks. The problem is complicated by the fact that one cannot rely on the cooperation of individual servers and routers to aid in the collection of network traffic measurements vital for these tasks. Hence we go for network monitoring and inference methods based on packet probing in the network. This paper presents an analysis of different inference methods for network characteristics to deal with shared congestion, packet forwarding priority, network tomography and evaluates each methodology based on packet loss rate and delay variance

Monitoring multicast traffic in heterogeneous networks

Estágio realizado no INESC - Porto e orientado pelo Prof. Doutor Ricardo MorlaTese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major Telecomunicações. Faculdade de Engenharia. Universidade do Porto. 200

Link Delay Inference in ANA Network

Estimating quality of service (QoS) parameters such as link delay distribution from the end-to-end delay of a multicast tree topology in network tomography cannot be achieved without multicast probing techniques or designing unicast probing packets that mimic the characteristics of multicast probing packets. Active probing is gradually giving way to passive measurement techniques. With the emergence of next generation networks such as Autonomic Network Architecture (ANA) network, which do not support active probing, a new way of thinking is required to provide network tomography support for such networks. This thesis is about investigating the possible solution to such problem in network tomography. Two approaches, queue model and adaptive learning model were implemented to minimized the uncertainty in the end-to-end delay measurements from passive data source so that we could obtain end-toend delay measurements that exhibit the characteristics of unicast or multicast probing packets. The result shows that the adaptive learning model performs better than the queue model. In spite of its good performance against the queue model, it fails to outperform the unicast model. Overall, the correlation between the adaptive learning model and multicast probing model is quite weak when the traffic intensity is low and strong when the traffic intensity is high. The adaptive model may be susceptible to low traffic. In general, this thesis is a paradigm shift from the investigation of ”deconvolution” algorithms that uncover link delay distributions to how to estimate link delay distributions without active probing.Master i nettverks- og systemadministrasjo