KnowMe: A Module to Improve the Efficiency of Resource Allocation in Data Center Networks
Bibliographic record
Abstract
In recent years, a new paradigm called network-application integration (NAI) has been introduced which enables applications to express their requirements to the network. By applying NAI in data center networks (DCNs), the DCN controller can better serve applications. We refer to this as data center network-application integration (DC-NAI). However, a critical issue in DC-NAI is how applications use the assigned resources. Selfish applications may under-utilize or over-utilize the assigned resources which can negatively impact the existing applications. To overcome this challenge, in this paper, we propose a DCN controller add-on module called KnowMe that takes the applications’ behavior into consideration and improves the efficiency of the resource allocation. The KnowMe module is composed of two components: the resource utilization prediction and the resource assignment learning. The first component predicts the future applications’ resource usage by looking at their previous resource usage behavior. The second component makes decisions about the applications’ resource requests considering the current network status. Simulation results confirm the efficiency of our proposed module and its ability to mitigate the selfish behavior of applications. The results show that under network resource limitation, the selfish applications in KnowMe-enabled DCNs experience 40% higher resource request rejection rate comparing to the well-behaved applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".