On the Performance of Primal/Dual Schemes for Congestion Control in Networks with Dynamic Flows
Bibliographic record
Abstract
Stability and fairness are two design objectives of congestion control mechanisms; they have traditionally been analyzed for long-lived flows (or elephants). It is only recently that short-lived flows (or mice) have received attention. Whereas stability has been established for the existing primal-dual based control mechanisms, the performance issue has been largely overlooked. We study utility maximization problems for networks with dynamic flows. In particular, we consider the case where sessions of each class results in flows that arrive according to a Poisson process and have a length given by a general distribution. The goal is to maximize the long-term expected system utility that is a function of the number of flows and the rate (identical within a given class) allocated to each flow. Our results show that, as long as the average amount of work brought by the flows is strictly within the network stability region, the rate allocation and stability issues are decoupled. While stability can be guaranteed by, for example, a FIFO policy, utility maximization becomes an unconstrained optimization that results in a static rate allocation for flows. We also provide a queueing interpretation of this seemingly surprising result and show that not all utility functions make sense for dynamic flows. Finally, we use simulation results to show that indeed the open-loop algorithm maximizes the expected system utility.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".