Self-organizing overlay networks for Autonomic Manager selection
Bibliographic record
Abstract
Recent work in network management has led to the incorporation of Autonomic Computing (AC) concepts in management tasks to reduce cost and complexity. IBM was the first to propose the concept of AC for systems management [1]. Entities called Autonomic Managers (AMs) run an Autonomic Control Loop (ACL) to perform self-management functions. Every AM is responsible for monitoring one or more elements. All nodes of an overlay network are autonomic entities but only a subset is active in the management process depending on available resources. This paper proposes an efficient self-organizing mechanism for the autonomic management of overlay networks that reduces delays and error ratios of sensed data. A game theory approach to solve the AM selection problem is used. The game is based on a modified regret matching scheme. Advantages are that the approach is highly distributed and incurs limited network overhead. More importantly, it adapts to the dynamic network conditions. Simulation results show that our scheme achieves high gains in terms of delays and error rates compared to a straightforward scheme where nodes report to the closest AM.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".