A passive method for estimating end-to-end TCP packet loss

Benko, Peter; Veres, András

doi:10.1109/glocom.2002.1189102

Cited by 62 publications

(41 citation statements)

References 4 publications

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“…Allman et al demonstrated how to estimate TCP loss rates from passive packet traces of TCP transfers taken close to the sender [20]. A related study examined passive packet traces taken in the middle of the network [21]. Network tomography based on using both multicast and unicast probes has also been demonstrated to be effective for inferring loss rates on internal links on end-to-end paths [22], [23].…”

Section: Related Workmentioning

confidence: 99%

A Geometric Approach to Improving Active Packet Loss Measurement

Sommers

Barford

Duffield

et al. 2008

IEEE/ACM Trans. Networking

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Abstract-Measurement and estimation of packet loss characteristics are challenging due to the relatively rare occurrence and typically short duration of packet loss episodes. While active probe tools are commonly used to measure packet loss on end-toend paths, there has been little analysis of the accuracy of these tools or their impact on the network. The objective of our study is to understand how to measure packet loss episodes accurately with end-to-end probes. We begin by testing the capability of standard Poisson-modulated end-to-end measurements of loss in a controlled laboratory environment using IP routers and commodity end hosts. Our tests show that loss characteristics reported from such Poisson-modulated probe tools can be quite inaccurate over a range of traffic conditions. Motivated by these observations, we introduce a new algorithm for packet loss measurement that is designed to overcome the deficiencies in standard Poisson-based tools. Specifically, our method entails probe experiments that follow a geometric distribution to (1) enable an explicit trade-off between accuracy and impact on the network, and (2) enable more accurate measurements than standard Poisson probing at the same rate. We evaluate the capabilities of our methodology experimentally by developing and implementing a prototype tool, called BADABING. The experiments demonstrate the trade-offs between impact on the network and measurement accuracy. We show that BADABING reports loss characteristics far more accurately than traditional loss measurement tools.

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Section: Related Workmentioning

confidence: 99%

A Geometric Approach to Improving Active Packet Loss Measurement

Sommers

Barford

Duffield

et al. 2008

IEEE/ACM Trans. Networking

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“…Our techniques for estimating link capacity measure the median dispersion of packet pairs sent from the sender to the target link, as in [25], [26]. Finally, we resolve ambiguities in loss rate estimates using techniques similar to [27]. Our approach to measuring RTT and loss rates is also similar to T-RAT [16].…”

Section: Related Workmentioning

confidence: 99%

“…We measure loss rates using retransmissions and a simple algorithm based on [27]. The algorithm starts by arranging packets of a flow, based on increasing timestamps.…”

Section: B Extracting Network Characteristicsmentioning

confidence: 99%

Swing: Realistic and Responsive Network Traffic Generation

Vishwanath

Vahdat

2009

IEEE/ACM Trans. Networking

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Abstract-This paper presents Swing, a closed-loop, networkresponsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traffic is statistically similar to the original traffic. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of our individual model parameters to accurately reproduce such burstiness. Finally, we explore Swing's ability to vary user characteristics, application properties, and wide-area network conditions to project traffic characteristics into alternate scenarios.

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“…A monitor is placed on a network link and passively observes packets passing through the link. One can then apply inference techniques on these observations to compute performance metrics [3], [10], [14], [13], [11], study protocol behavior [26], [11] and understand the dynamics of traffic demands in the network.…”

Section: Introductionmentioning

confidence: 99%

Formal Analysis of Passive Measurement Inference Techniques

Jaiswal¹,

Iannaccone²,

Kurose³

et al. 2006

Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

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Abstract-Verifying the accuracy of a passive measurementsbased inference technique under all possible network scenarios is a difficult challenge -the measurement point has limited observability of events along the path, and monitored paths can exhibit a wide range of network properties (packet loss, reordering, end-end delay, route changes). In this paper, we propose and apply formal verification techniques to exhaustively verify the correctness of an inference technique. We apply this approach to the problem of inferring packet retransmissions and reorderings from passively observed packets at a single measurement point. We define classification rules for this inference problem and, through a combination of model-checking and formal reasoning, uncover all possible events in the network for which the rules produce incorrect inferences. Our work is novel in its use of formal verification tools for evaluating inference techniques in network measurements.

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A passive method for estimating end-to-end TCP packet loss

Cited by 62 publications

References 4 publications

A Geometric Approach to Improving Active Packet Loss Measurement

A Geometric Approach to Improving Active Packet Loss Measurement

Swing: Realistic and Responsive Network Traffic Generation

Formal Analysis of Passive Measurement Inference Techniques

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