Reactive Estimation of Packet Loss Probability for IP-Based Video Services
Bibliographic record
Abstract
The advent of IP/MPLS based networks allows providers to implement integrated services packet network (ISPN) concepts for IPTV applications. IPTV systems require a high level of quality of service (QoS) in order to win customers which are subscribed with cable companies. Packet loss probability is one of the primary QoS parameters whose value affects the user experience and which provides a quantitative measure for customers' perception of the QoS factor in practical networks. Therefore, the online accurate measurement and estimation of the packet loss probability is a key issue to be addressed. Furthermore, the packet loss probability provides the feedback information which can be used in the network based control system to maintain it at a prescribed and negotiated value. In this paper, a reactive estimator (RE) of the packet loss probability is constructed. As all parameters related to the network traffic, the packet loss is a non-linear and non-Gaussian stochastic process. The proposed RE has the capability to adapt to variable stochastic contexts by employing one dynamic item based on the feedback of the real-time loss ratio measurement. A series of experiments are devised on a live network to evaluate the performance of the estimator under multiple traffic arrival models and within various buffer sizes. The numeric results show that the practical estimator is accurate enough to approximate the packet loss probability, such that it can be further used as a feedback parameter in a closed control loop which can keep the packet loss probability close to a prescribed value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".