13,342 research outputs found
Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking
P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols
Distributed Collaborative Monitoring in Software Defined Networks
We propose a Distributed and Collaborative Monitoring system, DCM, with the
following properties. First, DCM allow switches to collaboratively achieve flow
monitoring tasks and balance measurement load. Second, DCM is able to perform
per-flow monitoring, by which different groups of flows are monitored using
different actions. Third, DCM is a memory-efficient solution for switch data
plane and guarantees system scalability. DCM uses a novel two-stage Bloom
filters to represent monitoring rules using small memory space. It utilizes the
centralized SDN control to install, update, and reconstruct the two-stage Bloom
filters in the switch data plane. We study how DCM performs two representative
monitoring tasks, namely flow size counting and packet sampling, and evaluate
its performance. Experiments using real data center and ISP traffic data on
real network topologies show that DCM achieves highest measurement accuracy
among existing solutions given the same memory budget of switches
KISS: Stochastic Packet Inspection Classifier for UDP Traffic
This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications
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