A novel shared segment protection method for guaranteed recovery time
Bibliographic record
Abstract
Shared segment protection (SSP), compared to shared path protection (SPP) or shared link protection (SLP), provides an optimal protection configuration, since SSP can increase the number of connections sharing the same protection segments and can reduce the restoration time in case of single link failure. This paper provides a thorough study on SSP under the GMPLS-based recovery framework, where an effective survivable routing algorithm for SSP is proposed, called shared segment protection (SSP) algorithm. The main advantage of the SSP algorithm is to reduce the high computation complexity in solving the ILP formulation first introduced in P-H. Ho et al., (2004). With an efficient iterative approach the design space is significantly reduced by excluding all the links that result intolerably long routes. The tradeoff between the price (i.e., cost representing the amount of resources, and the blocking probability) and the restoration time is extensively studied by simulations on three networks with highly dynamic traffic. It is demonstrated that the SSP algorithm can be a powerful solution in the GMPLS-based recovery with a stringent delay upper bound for achieving high availability and restorability of the transport services. The comparison among the three protection types further verifies that SSP can yield significant advantages over SPP and SLP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".