Abstract
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.
Highlights
The internet was originally designed to be neutral and primarily provide service for time-insensitive traffic
The time elapsed since the previous recv() is greater than H · Round Trip Time (RTT); The number of received bytes is greater than 1 MSS and less frequent than K; where H is the smoothing parameter, and K is the ratio of the maximum number of bytes returned by recv() at a time to the total number of bytes received by the receiver during a Transmission Control Protocol (TCP) transfer
Where Losses is the number of estimated packet losses, Received_bytes is the total number of bytes received by the receiver during a TCP transfer, and MSS is the maximum segment size obtained through getsockopt()
Summary
The internet was originally designed to be neutral and primarily provide service for time-insensitive traffic. Symmetry 2019, 11, 442 the current network is information asymmetric, and end users know much less than network operators, internet service providers (ISP), and content providers [7] Such information asymmetry, especially in a non-neutral network environment, will seriously infringe the interests of end users. Especially in a non-neutral network environment, will seriously infringe the interests of end users This damage is reflected on the deterioration of the user’s quality of experience (QoE), and, for some ordinary users, the extent of such bad QoE naturally has no lower bound due to the lack of performance lower bound guarantees. That is, leveraging the TCP’s self-clocking mechanism [13], we mine the information contained in the burst-gap traffic patterns to dynamically estimate the two decisive parameters required by L-Rex. To rebalance the robustness, the effect of packet reordering is considered.
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