Consolidating flows with implicit deadlines for energy-proportional data center networks
Bibliographic record
Abstract
To reduce the energy consumption of a large number of network devices in a data center, energy-efficient schemes use various heuristics to consolidate traffic to fewer switches. Most of these works, however, ignore the flow-level performance, which is one of the most critical requirements in production data centers. Hence, flow rate allocation should be considered together with flow path selection to guarantee flow-level performance and in the meantime save energy of network devices. For this reason, we present a framework to ensure that the energy consumption for data center network (DCN) is proportional to the traffic and to guarantee the flow-level performance. Our solution consists of two components: (i) flow rate allocation to meet flows' deadlines and (ii) flow path selection to use fewer switches. We compare our framework with existing techniques under synthetic traffic patterns. Results show that our framework could save, on average, 20% of network energy than the always-on baseline, while maintaining the better flow-level performance, and achieving good running time and fault tolerance simultaneously.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".