On the energy-efficiency of a packet-level FEC based bufferless core optical network

F1 - Full Written Papers Referee

Greening Router Line-Cards via Dynamic Management of Packet Memory

Continued scaling of switching capacity in the Internet core is threatened by power considerations. Internet service providers face increased carbon footprint and operational costs, while router manufacturers encounter upper limits on switching capacity per rack. This paper studies the role of packet buffer memory on the power consumption of backbone routers. Our first contribution is to estimate from published datasheets the energy costs of static RAM/dynamic RAM packet-buffer memory, showing that it accounts for over 10% of power consumption in a typical router line-card; we then show, using empirical data from core and enterprise networks, that much of this memory is used for only a small fraction of time. Our second contribution is to develop a simple yet practical algorithm for putting much of the memory components to sleep and waking them as needed, while being able to control resulting traffic performance degradation in the form of packet loss during transient congestion. Finally, we conduct a comprehensive evaluation of our scheme, via analytical models pertaining to long-range-dependent traffic, using simulations of offline traffic traces taken from carrier/enterprise networks as well as online Transmission Control Protocol flows in ns2, and by implementing our scheme on a programmable-router test bed. This paper is the first to show the feasibility of, and energy savings from, dynamic management of packet buffer memory in core routers in the market today

Statistical Network Anomaly Detection: An Experimental Study

The number and impact of attack over the Internet have been continuously increasing in the last years, pushing the focus of many research activities into the development of effective techniques to promptly detect and identify anomalies in the network traffic. In this paper, we propose a performance comparison between two different histogram based anomaly detection methods, which use either the Euclidean distance or the entropy to measure the deviation from the normal behaviour. Such an analysis has been carried out taking into consideration different traffic features. The experimental results, obtained testing our systems over the publicly available MAWILAb dataset, point out that both the applied method and the chosen descriptor strongly impact the detection performance

Mining anomalies using traffic feature distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework