In search of simplicity: a self‐organizing group communication overlay
Bibliographic record
Abstract
Abstract Group communication primitives have broad utility as building blocks for distributed applications. The challenge is to create and maintain the distributed structures that support these primitives while accounting for volatile end‐nodes and variable network characteristics. Most solutions proposed to date rely on complex algorithms or on global information, thus limiting the scale of deployments and acceptance outside the academic realm. This article introduces a low‐complexity, self‐organizing solution for building and maintaining data dissemination trees, which we refer to as Unstructured Multi‐source Overlay (UMO). UMO uses traditional distributed systems techniques: layering, soft‐state, and passive data collection to adapt to the dynamics of the physical network and maintain data dissemination trees. The result is a simple, adaptive system with lower overheads than more complex alternatives. We implemented UMO and evaluated it on a 100‐node PlanetLab testbed and on up to 1024‐node emulated ModelNet networks. Extensive experimental evaluations demonstrate UMOs low overhead, efficient network usage compared with alternative solutions, and the ability to quickly adapt to network changes and to recover from failures. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".