Strengthening measurements from the edges: application-level packet loss rate estimation

Network users know much less than ISPs, Internet exchanges and content providers about what happens inside the network. Consequently users cannot either easily detect network neutrality violations or readily exercise their market power by knowledgeably switching ISPs. This paper contributes to the ongoing efforts to empower users by proposing two models to estimate -- via application-level measurements -- a key network indicator, i.e., the packet loss rate (PLR) experienced by FTP-like TCP downloads. Controlled, testbed, and large-scale experiments show that the Inverse Mathis model is simpler and more consistent across the whole PLR range, but less accurate than the more advanced Likely Rexmit model for landline connections and moderate PL

GPU Accelerated protocol analysis for large and long-term traffic traces

This thesis describes the design and implementation of GPF+, a complete general packet classification system developed using Nvidia CUDA for Compute Capability 3.5+ GPUs. This system was developed with the aim of accelerating the analysis of arbitrary network protocols within network traffic traces using inexpensive, massively parallel commodity hardware. GPF+ and its supporting components are specifically intended to support the processing of large, long-term network packet traces such as those produced by network telescopes, which are currently difficult and time consuming to analyse. The GPF+ classifier is based on prior research in the field, which produced a prototype classifier called GPF, targeted at Compute Capability 1.3 GPUs. GPF+ greatly extends the GPF model, improving runtime flexibility and scalability, whilst maintaining high execution efficiency. GPF+ incorporates a compact, lightweight registerbased state machine that supports massively-parallel, multi-match filter predicate evaluation, as well as efficient arbitrary field extraction. GPF+ tracks packet composition during execution, and adjusts processing at runtime to avoid redundant memory transactions and unnecessary computation through warp-voting. GPF+ additionally incorporates a 128-bit in-thread cache, accelerated through register shuffling, to accelerate access to packet data in slow GPU global memory. GPF+ uses a high-level DSL to simplify protocol and filter creation, whilst better facilitating protocol reuse. The system is supported by a pipeline of multi-threaded high-performance host components, which communicate asynchronously through 0MQ messaging middleware to buffer, index, and dispatch packet data on the host system. The system was evaluated using high-end Kepler (Nvidia GTX Titan) and entry level Maxwell (Nvidia GTX 750) GPUs. The results of this evaluation showed high system performance, limited only by device side IO (600MBps) in all tests. GPF+ maintained high occupancy and device utilisation in all tests, without significant serialisation, and showed improved scaling to more complex filter sets. Results were used to visualise captures of up to 160 GB in seconds, and to extract and pre-filter captures small enough to be easily analysed in applications such as Wireshark

A framework for network traffic analysis using GPUs

During the last years the computer networks have become an important part of our society. Networks have kept growing in size and complexity, making more complex its management and traffic monitoring and analysis processes, due to the huge amount of data and calculations involved. In the last decade, several researchers found effective to use graphics processing units (GPUs) rather than a traditional processors (CPU) to boost the execution of some algorithms not related to graphics (GPGPU). In 2006 the GPU chip manufacturer NVIDIA launched CUDA, a library that allows software developers to use their GPUs to perform general purpose algorithm calculations, using the C programming language. This thesis presents a framework which tries to simplify the task of programming network traffic analysis with CUDA to software developers. The objectives of the framework have been abstracting the task of obtaining network packets, simplify the task of creating network analysis programs using CUDA and offering an easy way to reuse the analysis code. Several network traffic analysis have also been developed

High-speed TCP flow record extraction using GPUs

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-015-1478-9Traffic analysis is an essential part of capacity planning, quality of service assurance, and reinforcement of security in current telecommunication networks. Traffic volume increases with network speed and the analysis of large traffic traces is computationally intensive. The paper presents, for the first time ever, a flow extraction software that allows to obtain complex TCP-aware flow records at 4.4 millions of packets per second in a single GPU. Such TCP flow records include number of retransmissions and duplicates per flow, whose calculation is very challenging to obtain at high-speed. Our software significantly increases the processing performance of the recently proposed high-speed sniffers based on commodity hardware and demonstrates the advantages of applying massively parallel processing devices for traffic analysis