QoS-aware service composition and adaptation in autonomic communication
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
Advents in network technology and distributed system design have propelled network communication service beyond best effort data delivery. With the rising complexity of network infrastructures and the need for on-demand provisioning operations, a high degree of self-sufficiency and automation is required in the network service infrastructure. Guided by the autonomic communication principle, this paper first presents an autonomic service provisioning framework for establishing quality-of-service (QoS)-assured end-to-end communication paths across administratively independent domains. Through graph abstraction, we show that the domain composition and adaptation problem could be reduced to the classic k-multiconstrained optimal path (MCOP) problem. In analyzing existing k-MCOP solutions, we show their inefficiencies when applied to the service provisioning context and establish a number of new domain composition and adaptation algorithms. These new algorithms are designed for the self-configuration, self-optimization, and self-adaptation of end-to-end network communications and can provide hard QoS guarantees over domains with relative QoS differentiations. Through in-depth experimentations, we compare the performance of our algorithms with classic k-MCOP solutions and demonstrate the effectiveness of our approach.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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