Useful Traffic Loss Rate Estimation Based on Network Layer Measurement

Lan, Haoliang; Ding, Wei; Gong, Jian

doi:10.1109/access.2019.2893951

Cited by 3 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to obtain the data required for regression analysis and PLMBSF evaluation, it is necessary to generate the target TCP flow and make it flow through the preset capture point so as to create a data capture scenario shown in Figure 1. In detail, the platform consists of four servers and ten clients located inside and outside JSERNET [26], respectively. Among them, the server configuration information is shown in Table 1, and number 1 to number 3 are communication servers, and number 4 is the storage server.…”

Section: Platformmentioning

confidence: 99%

See 1 more Smart Citation

Packet Loss Measurement Based on Sampled Flow

Lan

Wang

et al. 2021

Symmetry

Self Cite

View full text Add to dashboard Cite

This paper is devoted to further strengthening, in the current asymmetric information environment, the informed level of operators about network performance. Specifically, in view of the burst and perishability of a packet loss event, to better meet the real-time requirements of current high-speed backbone performance monitoring, a model for Packet Loss Measurement at the access network boundary Based on Sampled Flow (PLMBSF) is presented in this paper under the premise of both cost and real-time. The model overcomes problems such as the inability of previous estimation to distinguish between packet losses before and after the monitoring point, deployment difficulties and cooperative operation consistency. Drawing support from the Mathis equation and regression analysis, the measurement for packet losses before and after the monitoring point can be realized when using only the sampled flows generated by the access network boundary equipment. The comparison results with the trace-based passive packet loss measurement show that although the proposed model is easily affected by factors such as flow length, loss rate, sampling rate, the overall accuracy is still within the acceptable range. In addition, the proposed model PLMBSF, compared with the trace-based loss measurement is only different in the input data granularity. Therefore, PLMBSF and its advantages are also applicable to aggregated traffic.

show abstract

Section: Platformmentioning

confidence: 99%

“…• goodput represents the effective throughput, indicating the useful traffic [26] transferred from the source to the sink in a unit time;…”

mentioning

confidence: 99%

Packet Loss Measurement Based on Sampled Flow

Lan

Wang

et al. 2021

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accordingly, the controlled TCP transfer (called "controlled TCP transfer" because we had full control over the two communicating end-hosts) shown in Figure 6 is used to produce the wanted semantically complete TCP transfer. In our previous work [12], we implemented controlled TCP transfer for research purposes. Specifically, our experiments involved four servers (see Table 2 for configuration information) within JSERNET and ten client hosts in the outside of JSERNET.…”

Section: Controlled Tcp Transfersmentioning

confidence: 99%

“…Although the retransmitted data pkts is a good approximation of packet losses, the spurious retransmissions caused by flaws in TCP's retransmission schemes [14] make the retransmissions larger than the actual packet losses. Given this, in this paper, we get the real packet losses by comparing the packet traces at two communicating end-hosts [12]. Again, for the data packet performing IP fragmentation [28], as long as one fragment belonging to it is lost, the entire data packet is considered lost.…”

Section: Controlled Tcp Transfersmentioning

confidence: 99%

“…Accordingly, light and user-friendly application-level QoS measurement is conducive to promoting large-scale user installation and, thus, quickly reversing the information asymmetry [8]. Among the numerous key performance indicators (KPI) indicating QoS, application-level packet loss rate, as an important KPI, is widely studied and measured [9][10][11][12]. However, as said by Basso et al, "the collected data in the application layer is more coarse-grained than the data collected with kernel-level measurements" [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2020

View full text Add to dashboard Cite

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

show abstract

Useful Traffic Loss Rate Estimation Based on Network Layer Measurement

Cited by 3 publications

References 29 publications

Packet Loss Measurement Based on Sampled Flow

Packet Loss Measurement Based on Sampled Flow

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

Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

Contact Info

Product

Resources

About