Experience with Seattle: A Community Platform for Research and Education
Bibliographic record
Abstract
Hands-on experience is a critical part of research and education. Today's distributed testbeds fulfill that need for many students studying networking, distributed systems, cloud computing, security, operating systems, and similar topics. In this work, we discuss one such testbed, Seattle. Seattle is an open research and educational testbed that utilizes computational resources provided by end users on their existing devices. Unlike most other platforms, resources are not dedicated to the platform which allows a greater degree of network diversity and realism at the cost of programmability. Seattle is designed to preserve user security and to minimally impact application performance. We describe the architectural design of Seattle, and summarize our experiences with Seattle over the past few years as both researchers and educators. We have found that Seattle is very easy to adopt due to cross-platform support, and is also surprisingly easy for students to use. While there are programmability limitations, it is possible to construct complex applications integrated with real devices, networks, and users with Seattle as a core component. From an educational standpoint, Seattle has been shown not only to be useful as a teaching tool, it has been successful in variety of different systems classes at a variety of different types of schools. In our experience, when low-level programmability is not the main requirement, Seattle can supersede many existing testbeds for diverse educational and research tasks.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".