Capacity provisioning for schedulers with tiny buffers
Bibliographic record
Abstract
Capacity and buffer sizes are critical design parameters in schedulers which multiplex many flows. Previous studies show that in an asymptotic regime, when the number of traffic flows N goes to infinity, the choice of scheduling algorithm does not have a big impact on performance. We raise the question whether or not the choice of scheduling algorithm impacts the capacity and buffer sizing for moderate values of N (e.g., few hundred). For Markov-modulated On-Off sources and for finite N, we show that the choice of scheduling is influential on (1) buffer overflow probability, (2) capacity provisioning, and (3) the viability of network decomposition in a non-asymptotic regime. This conclusion is drawn based on numerical examples and by a comparison of the scaling properties of different scheduling algorithms. In particular, we show that the per-flow capacity converges to the per-flow long-term average rate of the arrivals with convergence speeds ranging from O (√log N/N) to O(1/N) depending on the scheduling algorithm. This speed of convergences of the required capacities for different schedulers (to meet a target buffer overflow probability) is perceptible even for moderate values of N in our numerical examples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".