Data-driven data center traffic control
Bibliographic record
Abstract
Recent networking research has identified that data-driven congestion control \n(CC) can be more efficient than traditional CC in data centers (DCs). Deep \nreinforcement \nlearning (RL), in particular, has the potential to learn optimal network \npolicies. However, RL suffers from instability and over-fitting, deficiencies \nwhich so far render it unacceptable for use in DC networks. We analyze \nthe requirements for data-driven policies to succeed in the DC context. \nAnd, we present a new emulator, Iroko, which supports different network \ntopologies, DC traffic engineering algorithms, and deployment \nscenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast \nand fair evaluation of RL against traditional algorithms under equal conditions. \nWe present initial benchmarks of three deep RL algorithms against TCP New Vegas \nand DCTCP. The results show that out-of-the-box algorithms are able to learn a \nCC policy with comparative performance to TCP in our emulator. We make Iroko \nopen-source and publicly available: https://github.com/dcgym/iroko.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".