2010 | OriginalPaper | Buchkapitel
Network DVR: A Programmable Framework for Application-Aware Trace Collection
verfasst von : Chia-Wei Chang, Alexandre Gerber, Bill Lin, Subhabrata Sen, Oliver Spatscheck
Erschienen in: Passive and Active Measurement
Verlag: Springer Berlin Heidelberg
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Network traces are essential for a wide range of network applications, including traffic analysis, network measurement, performance monitoring, and security analysis. Existing capture tools do not have sufficient built-in intelligence to understand these application requirements. Consequently, they are forced to collect
all
packet traces that might be useful at the finest granularity to meet a certain level of accuracy requirement. It is up to the network applications to process the per-flow traffic statistics and extract meaningful information. But for a number of applications, it is much more efficient to record packet sequences for flows that match some application-specific signatures, specified using for example regular expressions. A basic approach is to begin memory-copy (recording) when the first character of a regular expression is matched. However, often times, a matching eventually fails, thus consuming unnecessary memory resources during the interim. In this paper, we present a programmable
application-aware
triggered trace collection system called Network DVR that performs precisely the function of packet content recording based on user-specified trigger signatures. This in turn significantly reduces the number of memory copies that the system has to consume for valid trace collection, which has been shown previously as a key indicator of system performance [8]. We evaluated our Network DVR implementation on a practical application using 10 real datasets that were gathered from a large enterprise Internet gateway. In comparison to the basic approach in which the memory-copy starts immediately upon the first character match without
triggered-recording
, Network DVR was able to reduce the amount of memory-copies by a factor of over 500x on average across the 10 datasets and over 800x in the best case.