Realizing the advantages of optical reconfigurability and restoration with integrated optical cross-connects
Bibliographic record
Abstract
Optical layer capacity and unit cost improvements are basic to the rapid growth of Internet protocol (IP) networks. However, the new rapid reconfiguration and restoration capabilities of the optical layer have been sparingly utilized by IP network operators. This paper argues that this is consistent with the economics: restoration based on a "discrete" optical cross-connect (DOXC), i.e., one not integrated into wavelength-division multiplexing (WDM), incurs heavy interface costs. In addition, we argue that there are architectural and control issues that are roadblocks to IP exploitation of rapid optical layer reconfigurability. We then describe an architecture based on optical cross-connects (OXCs) integrated with the WDM (IOXCs), and one instantiation of this architecture using a class of degree-N optical add/drop multiplexers (OADMs), and propose a method to use these to provide efficient shared-capacity path-based restoration after link failures. A series of economic comparisons are made on both a 120-node hypothetical national network and a smaller express backbone network to compare hard-wired, DOXC-based, and IOXC-based optical networks when used to transport OC-192 IP traffic. The conclusion is that the IOXC-based architectures seem to be a promising way to introduce reconfigurability into the optical layer cost-effectively. These architectures also seem to be economically competitive with link restoration done in the IP layer, even if rapid reconfigurability is not an imposed requirement. (It should be noted that other IP layer failure modes are not included in the analysis.) Finally, we describe some of the control and management plane challenges introduced by these architectures. We also identify and describe several applications that leverage optical layer reconfigurability to benefit the IP Layer; however, these require some IP layer changes, which we mention.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".