Drift: A highly condensed emulation framework for mobile nodes in server clusters
Bibliographic record
Abstract
Prototyping application-layer algorithms in wireless networks is a lengthy and challenging process, involving either programming within a specific simulation platform, or deploying a real testbed that is neither flexible nor scalable. In this paper, we present Drift, a high-performance wireless emulation testbed that takes advantage of the benefits of both simulation and real implementation, while trying to avoid their drawbacks. As a highly condensed emulation infrastructure, Drift makes it possible to rapidly develop and validate large-scale wireless network application-layer protocols within a cluster computing environment. Unlike existing emulation testbeds, Drift features a fully decentralized architecture, an efficient message processing unit, and more accurate network models for mobile wireless nodes. It balances the fundamental trade-off between scalability and emulation accuracy, focusing on maximizing scalability with minimal loss of accuracy. Through baseline comparison and extensive experiments, we find that Drift is able to accommodate thousands of emulated nodes per server host (in contrast to only tens of nodes typically seen in existing emulation tools), while maintaining comparable accuracy to packet level simulators. Drift will be released as an open source platform.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".