Automatic lightpath service provisioning with an adaptive protected working capacity envelope based on p-cycles
Bibliographic record
Abstract
The problem of automatic provisioning of survivable service paths in a transport network in the face of random arrivals and departures, and fundamental traffic uncertainty, is a challenging and interesting problem for research. Essentially to date, only one basic approach is considered, that of provisioning a working path and then explicitly arranging a shared disjoint backup path for protection. This is done on every new connection arrival and is heavily dependent on synchronized network state databases and state dissemination to keep such databases current. The authors described an alternate approach to simplify the process of automatic survivable service provisioning scheme from the end users point of view. Under an adaptive protected working capacity envelope (AP-WCE), statistically stationary but random demand patterns require little or no state dissemination and protected service routing is no different than shortest path working routing. But under non-stationary evolution of the traffic load both spatially and temporally, a slow-acting background process of APWCE reoptimization occurs which keeps the logical configuration of both working envelope and protection overlay always as well matched to the actual traffic load pattern as possible within the finite amount of total as-built transmission capacity. The scheme is attractive as it is easily implemented by existing network control systems and is inherently more scalable to large networks and fast random demands than the current shared-backup scheme because no signaling or state update relating to protection is required on the timescale of individual connection requests. Any such signaling arises only on the timescale of the non-stationary evolution of the traffic load pattern itself.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".