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In this work, we focus on analytics to compare measurements referring to different datasets, i.e., that help us to quickly pinpoint eventual performance differences between different populations of users. The latter permit a continuous monitoring by simply observing traffic, a challenging task given the several Gbps currently carried by Analytics to highlight and quantify statistical differences The former let the network administrator run on-demand specific tests at the expense of an increased network load. As a result, several tools are available for both active and passive measurements. Measurements have always played a central role to guide traffic management, to improve network and application design, and, in general, to understand the Internet. Section snippets Introduction and motivation In addition, we use the collected data to derive useful figures for the proper dimensioning of the CGN and the configuration of its parameters in order to avoid impairments on end-users’ experience. Results show that CGN does not impair quality of service in the analyzed ISP deployment. At last, we contrast figures of customers being offered public or private addresses, and look for statistically significant differences.

#STEFANO TRAVERSO DOWNLOAD#

Then, we derive higher level statistics that are significant for the end-users, e.g., TCP connection setup time, HTTP response time, or BitTorrent average download throughput. We first extract detailed per-flow information by processing packets from live links.

Since CGN may introduce connectivity issues and performance degradation, we process a large dataset of passive measurements collected from an ISP using CGN for part of its customers.

The ultimate goal is to highlight statistically significant differences that could be useful to flag to incidents for the network manager.Īfter introducing the methodology, we apply it to assess the impact of Carrier-Grade NAT (CGN), a technology that Internet Service Providers (ISPs) deploy to limit the usage of expensive public IP addresses.

In particular, we design and implement analytics that, based on a filtering process and on the building of empirical distributions, enable the comparison between two generic collections, e.g., data gathered from two different vantage points, from different populations, or at different times. In this paper, we engineer a methodology to extract, collect and process passive traffic traces. When considering to passively collect and then process network traffic traces, the need to analyze raw data at several Gbps and to extract higher level indexes from the stream of packets poses typical BigData-like challenges.

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