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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".