Bibliographic record
Abstract
Communication delay is a key source of uncertainty in distributed systems. Existing approaches to reduce this uncertainty focus on maintaining sufficient surplus bandwidth; applications, on their part, are designed in ways to tolerate certain degree of uncertainty in communication delays. This leads to contention between the goals of optimal utilization and acceptable delays. We argue that the multi-owned nature of today's networks offers opportunities to reason about and scalably control networks at a fine grain. An explicit treatment of network resource ownership and trade allows reasoning about acceptable delays. This can lead to scalable mechanisms for fine-grained accounting and reification of control, which make it possible to quantify and control network utilization. We bring together ownership, fine-grained accounting, and reification of control in a model for resource acquisition and control called CyberOrgs. CyberOrgs encapsulate distributed computations with resources required for their execution. A CyberOrg acquires resources required by its computations by buying them from other cyberorgs using eCash. We present a novel approach for implementing finegrained network resource control based on the CyberOrgs model. A prototype implementation is described with experimental results illustrating the effectiveness of control.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".