Missing Internet Traffic Reconstruction using Compressive Sampling

Missing traffic is a commonly problem in large-scale network. Because the traffic information is needed by network engineering task for network monitoring, there are several methods that recover the missing problem. In this paper, we proposed missing internet traffic reconstruction based on compressive sampling. The main contributions of this study are as follows: (i) explore the influence of the six missing patterns on the performance of the traffic matrix reconstruction algorithm; (ii) trace the link sensitivity; and (iii) detect the time sensitivity of the network. Using Abilene data, the simulation results show that compressive sampling can perform internet traffic monitoring such as reconstruction from missing traffic, finding link sensitivity, and detecting time sensitivity.

On the intertwining between capacity scaling and TCP congestion control

Recent works advocate the possibility of improving energy efficiency of network devices by modulating switching and transmission capacity according to traffic load. However, addressing the trade-off between energy saving and Quality of Service (QoS) under these approaches is not a trivial task, specially because most of the traffic in the Internet of today is carried by TCP, and is hence adaptive to the available resources. In this paper we present a preliminary investigation of the possible intertwining between capacity scaling approaches and TCP congestion control, and we show how this interaction can affect performance in terms of both energy saving and QoS

Self-Learning Classifier for Internet traffic

Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. In this paper, we propose SeLeCT, a Self-Learning Classifier for Internet traffic. It uses unsupervised algorithms along with an adaptive learning approach to automatically let classes of traffic emerge, being identified and (easily) labeled. SeLeCT automatically groups flows into pure (or homogeneous) clusters using alternating simple clustering and filtering phases to remove outliers. SeLeCT uses an adaptive learning approach to boost its ability to spot new protocols and applications. Finally, SeLeCT also simplifies label assignment (which is still based on some manual intervention) so that proper class labels can be easily discovered. We evaluate the performance of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents. Our experiments show that SeLeCT achieves overall accuracy close to 98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to help discovering new protocols and applications in an almost automated fashio

Uncovering the big players of the web

In this paper we aim at observing how today the Internet large organizations deliver web content to end users. Using one-week long data sets collected at three vantage points aggregating more than 30,000 Internet customers, we characterize the offered services precisely quantifying and comparing the performance of different players. Results show that today 65% of the web traffic is handled by the top 10 organiza- tions. We observe that, while all of them serve the same type of content, different server architectures have been adopted considering load bal- ancing schemes, servers number and location: some organizations handle thousands of servers with the closest being few milliseconds far away from the end user, while others manage few data centers. Despite this, the performance of bulk transfer rate offered to end users are typically good, but impairment can arise when content is not readily available at the server and has to be retrieved from the CDN back-en

A QoS monitoring system in a heterogeneous multi-domain DVB-H platform

The MobileTV, IPTV, and DVB standards (DVB-H/T) have been defined to offer mobile users interactive multimedia services with quality of service (QoS) consistency analogous to TV services. However, the market has yet to provide effective and economical solutions for the real-time delivery of such services to the corresponding transmitters over multi-domain IP networks. The monitoring system proposed in this paper enables the QoS in the IP networks involved in the delivery of real-time multimedia content to the transmitters to be ascertained. The system utilizes the QoS parameters defined in MPEG-2 Transport Streams to detect problems occurring in the heterogeneous multi-domain IP networks. The ability to detect problems having an adverse effect on QoS allows appropriate control actions to be determined to recover the QoS across the composite IP network. The design and implementation of the proposed QoS-Monitoring system (QoS-MS) is presented, followed by analysis of experimental results that demonstrate the feasibility of the system