Download PDF by Baek-Young Choi: Network Monitoring in High Speed Networks

By Baek-Young Choi

ISBN-10: 1461401186

ISBN-13: 9781461401186

Community tracking serves because the foundation for a large scope of community, engineering and administration operations. distinctive community tracking consists of analyzing each packet traversing in a community. even if, this isn't possible with destiny high-speed networks, because of major overheads of processing, storing, and moving measured facts. community tracking in excessive velocity Networks provides actual size schemes from either site visitors and function views, and introduces adaptive sampling recommendations for numerous granularities of site visitors dimension. The recommendations permit tracking platforms to regulate the accuracy of estimations, and adapt sampling chance dynamically in line with site visitors stipulations. the problems surrounding community delays for sensible functionality tracking are mentioned within the moment a part of this booklet. Case stories according to genuine operational community strains are supplied all through this publication. community tracking in excessive velocity Networks is designed as a secondary textual content or reference e-book for advanced-level scholars and researchers focusing on machine technology and electric engineering. execs operating in the networking also will locate this booklet important.

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SCV. 12 shows the relation between SCV and traffic load in a scatter plot using the traces. , larger traffic load), the SCV of packet sizes tends to be smaller. Since the lower SCV leads to a smaller number of required packet samples, the adaptive random sampling is likely to offer a higher rate of sampled data reduction in times of high load, while providing the desired degree of accuracy in traffic load estimation. In other words, when a high-speed link is highly utilized, our adaptive sampling technique results in fewer packets to be sampled, thereby reducing the burden (both in terms of processing and storage) on the traffic monitoring and measurement device (whether on-board a router or off-board).

7) i=1 where ai , i = 1, . . , u, are the model parameters, and esk is the uncorrelated error (which we refer to as the prediction error). The error term esk follows a normal distribution with mean 0 and variance var(esk ) = σS2s (1 − ∑ui=1 asi ρSsk ,i ). Here ρSsk ,i k is the lag-i autocorrelation of Sks ’s. The model parameters ai , i = 1, . . 8) in terms of v past values of Sis ’s, where v ≥ 1 is a configurable parameter independent of u, and is typically referred to as the memory size. 22 2 Load Characterization and Measurement u ρh = ∑ ai ρh−i , where h = v, .

Hence, the optimal sampling probability also varies over time. This suggests that static sampling with fixed sampling probability may result in either erroneous undersampling or unnecessary oversampling. In other words, static sampling cannot capture the traffic dynamics accurately or efficiently. This motivates us to develop an adaptive random sampling technique that attempts to minimize the sampling frequency while ensuring that the sampling error is bounded. 1 Bounding Sampling Errors in Traffic Load Estimation Traffic load is the sum of the sizes of packets arriving during a certain time interval.

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Network Monitoring in High Speed Networks by Baek-Young Choi


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