Application-Level Packet Loss Rate Measurement Based on Improved L-Rex Model

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.