Distributed Systems in the Wild: The Theoretical Foundations and Experimental Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modernizing experimentation in system-oriented courses such as computer networks and distributed systems is often challenging due to the raw and complex nature of infrastructure testing. In these practical courses, students not only need access to network layers and system kernels, but they often need to reason about consistency issues associated with the distributed nature of these experiments. This paper outlines the pros and cons of redesigning a traditional distributed systems course to incorporate modern experimental facilities for deploying distributed systems, such as Emulab, Seattle and Planet Lab. The possibility of giving students practical and relevant experience coupled with theoretical foundations is explored by considering traditional learning outcomes in the context of new course assignment objectives. A proposed set of experiments, along with their potential pitfalls and shortcomings, provide a basis for an evaluation of the trade-offs of studying distributed systems in the wild.
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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 | 0.001 | 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 | 0.000 |
| 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 it