Model Predictive Congestion Control for Data Center Networks
Bibliographic record
Abstract
This paper is concerned with congestion control (CC) of data center networks (DCN) from a control theoretical perspective. We first establish a discrete-time state-space model to capture the behaviors between the queue length and congestion window (CWND) size. Leveraging this model, we propose a model predictive congestion control (MPCC) algorithm in remote direct memory access-enabled DCNs. As a window-based CC, MPCC dynamically adjusts the flow's CWND size according to the observed network conditions. To further enhance MPCC performance, an explicit MPCC (EMPCC) is also constructed, which can be pre-solved offline to reduce online computational cost guided by the explicit model predictive control (MPC) method. Through experiments ranging from bursty incast to real-world traffic patterns, we demonstrate that (E)MPCC significantly reduces occupied switch queue length and flow completion time while maintaining flow fairness and throughput, outperforming other well-known and widely adopted DCN-special CC algorithms such as DCTCP, DCQCN, TIMELY, SWIFT, and HPCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".