Buffer Management for Aggregated Streaming Data with Packet Dependencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many applications the traffic traversing the network has inter-packet dependencies due to application-level encoding schemes. For some applications, e.g., multimedia streaming, dropping a single packet may render useless the delivery of a whole sequence. In such environments, the algorithm used to decide which packet to drop in case of buffer overflows must be carefully designed, to avoid goodput degradation. We present a model that captures such inter-packet dependencies, and design algorithms for performing packet discards. Traffic consists of an aggregation of multiple streams, each of which consists of a sequence of inter-dependent packets. We provide two guidelines for designing buffer management algorithms for this problem, and demonstrate the effectiveness of these criteria. We devise an algorithm according to these guidelines and evaluate its performance analytically, using competitive analysis. We also present a simulation study that shows that the performance of our algorithm is within a small fraction of the performance of the best offline algorithm.
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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 | 0.001 | 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 it