Mapping approach for multiscale emulation networks in heterogeneous environments
Bibliographic record
Abstract
As real network environments become increasingly complex, the scale of network emulation topologies is expanding. To address this problem, on the one hand, the multiscale integration emulation approach can effectively account for emulation fidelity and computational overhead; on the other hand, multiple heterogeneous physical clusters in parallel can provide a scalable resource environment. An important focus of current research is determining how to better map multiscale network emulation topologies to heterogeneous physical clusters while enhancing resource utilization and load balance. For this purpose, this paper proposes a multiscale integration mapping method for heterogeneous environments (MIM-HE) that uses the multiscale integration approach for mapping and realizes network mapping based on heterogeneous physical clusters. MIM-HE increases the feasible scale of emulation. Experiments show that compared with a method of load balancing based on METIS (MLBM) and random network mapping (RNM), MIM-HE reduces the load imbalance index by 44.68% and 81.81%, respectively, and the remote throughput index by 15.01% and 49.18%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".