Strategies for fault tolerance in optical grid networks
Bibliographic record
Abstract
The need for powerful computing resources as well as capabilities for storage and transmission of large amounts of data in a number of application areas have led to the emergence of optical grids as a natural, cost-effective platform for supporting such applications. As a result there is also an increasing need for strategies and techniques designed to achieve fault tolerance in optical grid networks. Design for fault tolerance in both grid computing and optical networks are mature, well-researched fields in their own right. However, survivability in optical grids should not be treated merely as a concatenation of techniques developed separately in these two disciplines. Rather, it would be beneficial, in terms of resource availability as well as cost-effectiveness, to develop an integrated approach that takes into consideration the allocation of both computing and networking resources jointly. In this paper, we review the state-of-the-art techniques and approaches that have been proposed in the literature, for designing survivable optical grid networks. We also discuss some challenges, identify some open problems and outline future research directions for developing an integrated approach to fault tolerance in optical grids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".