Understanding the Properties of the BitTorrent Overlay

In this paper, we conduct extensive simulations to understand the properties of the overlay generated by BitTorrent. We start by analyzing how the overlay properties impact the efficiency of BitTorrent. We focus on the average peer set size (i.e., average number of neighbors), the time for a peer to reach its maximum peer set size, and the diameter of the overlay. In particular, we show that the later a peer arrives in a torrent, the longer it takes to reach its maximum peer set size. Then, we evaluate the impact of the maximum peer set size, the maximum number of outgoing connections per peer, and the number of NATed peers on the overlay properties. We show that BitTorrent generates a robust overlay, but that this overlay is not a random graph. In particular, the connectivity of a peer to its neighbors depends on its arriving order in the torrent. We also show that a large number of NATed peers significantly compromise the robustness of the overlay to attacks. Finally, we evaluate the impact of peer exchange on the overlay properties, and we show that it generates a chain-like overlay with a large diameter, which will adversely impact the efficiency of large torrents

Enhancing Application Identification By Means Of Sequential Testing

Abstract. One of the most important challenges for network administrators is the identification of applications behind the Internet traffic. This identification serves for many purposes as in network security, traffic engineering and monitoring. The classical methods based on standard port numbers or deep packet inspection are unfortunately becoming less and less efficient because of encryption and the utilization of non standard ports. In this paper we come up with an online iterative probabilistic method that identifies applications quickly and accurately by only using the size of packets. Our method associates a configurable confidence level to the port number carried in the transport header and is able to consider a variable number of packets at the beginning of a flow. By verification on real traces we observe that even in the case of no confidence in the port number, a very high accuracy can be obtained for well known applications after few packets were examined

Swarming Overlay Construction Strategies

Swarming peer-to-peer systems play an increasingly instrumental role in Internet content distribution. It is therefore important to better understand how these systems behave in practice. Recent research efforts have looked at various protocol parameters and have measured how they affect system performance and robustness. However, the importance of the strategy based on which peers establish connections has been largely overlooked. This work utilizes extensive simulations to examine the default overlay construction strategy in BitTorrent systems. Based on the results, we identify a critical parameter, the maximum allowable number of outgoing connections at each peer, and evaluate its impact on the robustness of the generated overlay. We find that there is no single optimal value for this parameter using the default strategy. We then propose an alternative strategy that allows certain new peer connection requests to replace existing connections. Further experiments with the new strategy demonstrate that it outperforms the default one for all considered metrics by creating an overlay more robust to churn. Additionally, our proposed strategy exhibits optimal behavior for a well-defined value of the maximum number of outgoing connections, thereby removing the need to set this parameter in an ad-hoc manner

Design and Evaluation of Parallel and Scalable Machine Learning Research in Biomedical Modelling Applications

The use of Machine Learning (ML) techniques in the medical field is not a new occurrence and several papers describing research in that direction have been published. This research has helped in analysing medical images, creating responsive cardiovascular models, and predicting outcomes for medical conditions among many other applications. This Ph.D. aims to apply such ML techniques for the analysis of Acute Respiratory Distress Syndrome (ARDS) which is a severe condition that affects around 1 in 10.000 patients worldwide every year with life-threatening consequences. We employ previously developed mechanistic modelling approaches such as the “Nottingham Physiological Simulator,” through which better understanding of ARDS progression can be gleaned, and take advantage of the growing volume of medical datasets available for research (i.e., “big data”) and the advances in ML to develop, train, and optimise the modelling approaches. Additionally, the onset of the COVID-19 pandemic while this Ph.D. research was ongoing provided a similar application field to ARDS, and made further ML research in medical diagnosis applications possible. Finally, we leverage the available Modular Supercomputing Architecture (MSA) developed as part of the Dynamical Exascale Entry Platform~- Extreme Scale Technologies (DEEP-EST) EU Project to scale up and speed up the modelling processes. This Ph.D. Project is one element of the Smart Medical Information Technology for Healthcare (SMITH) project wherein the thesis research can be validated by clinical and medical experts (e.g. Uniklinik RWTH Aachen).Notkun vélnámsaðferða (ML) í læknavísindum er ekki ný af nálinni og hafa nokkrar greinar verið birtar um rannsóknir á því sviði. Þessar rannsóknir hafa hjálpað til við að greina læknisfræðilegar myndir, búa til svörunarlíkön fyrir hjarta- og æðakerfi og spá fyrir um útkomu sjúkdóma meðal margra annarra notkunarmöguleika. Markmið þessarar doktorsrannsóknar er að beita slíkum ML aðferðum við greiningu á bráðu andnauðarheilkenni (ARDS), alvarlegan sjúkdóm sem hrjáir um 1 af hverjum 10.000 sjúklingum á heimsvísu á ári hverju með lífshættulegum afleiðingum. Til að framkvæma þessa greiningu notum við áður þróaðar aðferðir við líkanasmíði, s.s. „Nottingham Physiological Simulator“, sem nota má til að auka skilning á framvindu ARDS-sjúkdómsins. Við nýtum okkur vaxandi umfang læknisfræðilegra gagnasafna sem eru aðgengileg til rannsókna (þ.e. „stórgögn“), framfarir í vélnámi til að þróa, þjálfa og besta líkanaaðferðirnar. Þar að auki hófst COVID-19 faraldurinn þegar doktorsrannsóknin var í vinnslu, sem setti svipað svið fram og ARDS og gerði frekari rannsóknir á ML í læknisfræði mögulegar. Einnig nýtum við tiltæka einingaskipta högun ofurtölva, „Modular Supercomputing Architecture“ (MSA), sem er þróuð sem hluti af „Dynamical Exascale Entry Platform“ - Extreme Scale Technologies (DEEP-EST) verkefnisáætlun ESB til að kvarða og hraða líkanasmíðinni. Þetta doktorsverkefni er einn þáttur í SMITH-verkefninu (e. Smart Medical Information Technology for Healthcare) þar sem sérfræðingar í klíník og læknisfræði geta staðfest rannsóknina (t.d. Uniklinik RWTH Aachen)

Performance analysis under finite load and improvements for multirate 802.11

Automatic rate adaptation in CSMA/CA wireless networks may cause drastic throughput degradation for high speed bit rate stations (STAs). The CSMA/CA medium access method guarantees equal long-term channel access probability to all hosts when they are saturated. In previous work it has been shown that the saturation throughput of any STA is limited by the saturation throughput of the STA with the lowest bit rate in the same infrastructure. In order to overcome this problem, we ¯rst introduce in this paper a new model for ¯nite load sources with multirate capabilities. We use our model to investigate the throughput degradation outside and inside the saturation regime. We de¯ne a new fairness index based on the channel occupation time to have more suitable de¯nition of fairness in multirate environments. Further, we propose two simple but powerful mechanisms to partly bypass the observed decline in performance and meet the proposed fairness. Finally, we use our model for ¯nite load sources to evaluate our proposed mechanisms in terms of total throughput and MAC layer delay for various network con¯gurations

Application-Aware Model for Peer Selection

We introduce in this paper the notion of application-aware optimal peer selection. With the advent of P2P and overlay networks, many applications of our days need to select the best peer to contact, either to transfer some data or to be positioned in an overlay network. This selection is still with no clear solution given the heterogeneity of the Internet in terms of path characteristics and access link speed, and the diversity of application requirements. Most of existing protocols rely on simple heuristics as for example choosing the closest peer in terms of delay. We believe that the selection of the best peer should be a function of more parameters (delay, bandwidth, loss rate) and subject to some application utility function (e.g., delay vs. bandwidth). This work aims at motivating the need for taking application requirements coupled with network path measurements into account during the peer selection process. The work consists of running extensive measurements over the PlanetLab overlay network and comparing different peer selection policies. One observation made in this work is that over PlanetLab, the best peer to contact is not always the closest one and it changes with the application needs. Another observation is that path characteristics are not strongly correlated with each other (e.g., smaller delay does not always mean larger bandwidth) which makes their separate use insufficient

A Multi-task Adaptive Monitoring System Combining Different Sampling Primitives

International audienceTraffic measurement and analysis are crucial management activities for network operators. With the increase in traffic volume, operators resort to sampling primitives to reduce the measurement load. Unfortunately, existing systems use sampling primitives separately and configure them statically to achieve some performance objective. It becomes then important to design a new system that combines different existing sampling primitives together to support a large spectrum of monitoring tasks while providing the best possible accuracy by spatially correlating measurements and adapting the configuration to traffic variability. In this paper, and to prove the interest of the joint approach, we introduce an adaptive system that combines two sampling primitives, packet sampling and flow sampling, and that is able to satisfy multiple monitoring tasks. Our system consists of two main functions: (i) a global estimator that investigates measurements done by the different sampling primitives in order to deal with multiple monitoring tasks and to construct a more reliable global estimator while providing visibility over the entire network; (ii) an optimization method based on overhead prediction that allows to reconfigure monitors according to accuracy requirements and monitoring constraints. We present an exhaustive experimental methodology with different monitoring tasks in order to assess the performance of our system. Our experimentations are done on our MonLab platform that we developed for the purpose of this research

QoE-driven Cache Placement for Adaptive Video Streaming: Minding the Viewport

International audienceTo handle the increasing demand for video streaming, ISP's and service providers use edge servers to cache video content to reduce the rush on their servers, balance the load between them and over the network, and smooth out the traffic variability. The dynamic adaptive streaming over HTTP protocol (DASH) makes videos available in multiple representations, and end-users can switch video resolution as a function of their network conditions and terminal display capacity (e.g., bandwidth, screen resolution). In this context, we study a viewportaware caching optimization problem for dynamic adaptive video streaming that appropriately considers the client viewport size and access speed, the join time, and the characteristics of videos. We formulate and study the proposed optimization problem as an Integer Linear Program (ILP) that balances minimal join time and maximal visual experience, subject to the cache storage capacity. Our framework sheds light on optimal caching performance. Our proposed heuristic provides guidelines on the videos, and the representations of each video, to cache based on the video popularity, its encoding information, and the distribution of end-user display capacity and access speed in a way to maximize the overall end-user QoE

TICP: TCP-friendly Information Collecting Protocol

We present and validate TICP, a TCP-friendly reliable transport protocol to collect information from a large number of receivers spread over the Internet. TICP is a stand-alone protocol that can be used by any application requiring the collection of information: quality of reception in a multicast session, numbering of population, weather monitoring, etc. The protocol does not impose any constraint on the nature of the collected information. It ensures two main things: (i) the information to collect arrives entirely and correctly at the source where it is stored to be treated later, and (ii) the implosion at the source and the congestion of the network are avoided by controlling the rate at which receivers send their information. The congestion control part of TICP is designed with the main objective to be friendly with applications using TCP. We implement TICP in ns-2, and we validate that it allows to quickly and reliably collect information from receivers, while avoiding network congestion and being fair with competing traffic

Unveiling the end-user viewport resolution from encrypted video traces

International audienceVideo streaming is without doubt the most requested Internet service, and main source of pressure on the Internet infrastructure. At the same time, users are no longer satisfied by the Internet's best effort service, instead, they expect a seamless service of high quality from the side of the network. As result, Internet Service Providers (ISP) engineer their traffic so as to improve their end-users' experience and avoid economic losses. Content providers from their side, and to enforce customers privacy, have shifted towards end-to-end encryption (e.g., TLS/SSL). Video streaming relies on the dynamic adaptive streaming over HTTP protocol (DASH) which takes into consideration the underlying network conditions (e.g., delay, loss rate, and throughput) and the viewport capacity (e.g., screen resolution) to improve the experience of the end user in the limit of the available network resources. In this work, we propose an experimental framework able to infer fine-grained video flow information such as chunk sizes from encrypted YouTube video traces. We also present a novel technique to separate video and audio chunks from encrypted traces based on Gaussian Mixture Models (GMM). Then, we leverage our dataset to train models able to predict the class of viewport (either SD or HD) per video session with an average 92% accuracy and 85% F1-score. The prediction of the exact viewport resolution is also possible but shows a lower accuracy than the viewport class