Availability-Aware Design in Mesh Networks With Failure-Independent Path-Protecting $p$-Cycles
Bibliographic record
Abstract
Failure-independent path-protecting (FIPP)p-cycle is an extension of the span-protectingp-cycle, and an alternative approach for providing fully pre-connected protection paths with end-to-end failure-independent path protection (Kodian and Gorver,J.ofLightwaveTechnol.,vol. 23, no. 10, pp. 3241-3259). We study the unavailability of end-to-end traffic in FIPP-based mesh networks, which are designed to protect against single failures, and present an availability-aware network design method. Our design method allocates FIPPp-cycles such that the end-to-end unavailability of the protected demands is bounded by an upper limit which we can control. Our study will also focus on determining whether FIPPp-cycles will maintain their resource efficiency advantages over spanp-cycles when the network design is based on limiting the unavailability. Our results first show that the length of the FIPPp-cycle plays a vital role in determining the availability of the working path(s). Similar to span-protectingp-cycles, higher service working path(s) availability is obtained when the FIPPp-cycle(s) contains fewer hops. Results also indicate the important role of the number of demands protected by the same FIPPp-cycle. We notice that the higher the desired availability is, the less efficient the FIPP method becomes. This relationship is due to the fact that, to achieve higher service availability, the design will limit the number of demands sharing the same FIPP cycle. Accordingly, we affirm that, when the network design limits the service unavailability, FIPP tends to be less efficient, and its redundancy is 8-13% higher than span-protectingp-cycles. Additionally, we observe that, when we do not limit the unavailability, the average availability for span-protectingp-cycles tends to be more than the FIPPp-cycle method. We present our findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".