The current network is information asymmetric, about what happens in the network, and end users know much less than the Internet service provider. Especially, in a non-neutral network, for commercial interests, the operator's behavior of adjusting network configuration and controlling data transmission priority will seriously infringe the vital interests of end users. In order to rebalance such information asymmetry to empower users, researchers have been committed to the study of end-user network performance measurements. In this paper, we contribute to the ongoing efforts to empower users by defining and estimating the useful traffic loss rate (UTLR) experienced by a Transfer Control Protocol connection. The work presented in this paper is inspired by our empirical observation that what the operator's behavior ultimately affects is the link quality, and therefore, a good characterization of the impact of link quality changes on user's quality of experience (QoE) is the prerequisite for empowering users. To this end, the metric UTLR is first defined in this paper to better characterize the impact of link quality changes on user's QoE, and then, we construct a series of heuristic rules with the receiver network layer information to estimate UTLR. To improve the estimation accuracy, the effects of insufficient duplicate ACKs, static round trip time, enlarged loss period, repeated packet losses, and identification field cyclic reuse are also considered. Finally, the gradual and rigorous experiments show that the proposed method achieves a stable and precise estimation for UTLR over the entire measured parameter space. INDEX TERMSInformation asymmetric, network neutrality, quality of experience, performance measurement, useful traffic loss rate.
The current network information asymmetry is infringing the interests of end users in non-neutral network environment. To empower users, application-level performance measurements are usually used to rebalance the information asymmetry currently disfavoring users. Application-level packet loss rate, as an important key performance indicator (KPI), receives extensive attention in academia. However, every coin has its two sides. Although the application-level packet loss estimation is simple and nonintrusive, since the information available from the application layer is relatively scarce compared to the lower layers, the estimation accuracy has no guarantee. In view of this, taking the L-Rex model as the cut-in spot, this paper focuses on leveraging the self-clocking mechanism of Transmission Control Protocol (TCP) to improve the accuracy of application-level loss estimation. Meanwhile, owing to the dynamically estimated parameters and the weakened packet reordering impact, the robustness of estimation is also expected to be improved accordingly. Finally, a series of comparative experiments at the practically relevant parameter space show that the enhanced approach and strategy detailed in this paper are effective.
Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.
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