Memory-aware SLA-based mechanism for shared-mesh WDM networks
Bibliographic record
Abstract
The paper presents a dynamic provisioning mechanism through which service providers can exploit the unused allowable down time of the connections to serve the additional upcoming requests. The algorithm work based on the holding time of the connections and the failure arrival rate over the selected primary or backup paths. The proposed mechanism in this paper routes the requests in a way that any service level agreement (SLA) violation is either avoided or minimized. To achieve this goal, the already established paths are flagged with a newly proposed path metric, path risk factor, to create a memory-aware mechanism of paths' history showing the risk tolerance to SLA violation. This path attribute can be disseminated over the network as a metric of prospective connections. The algorithm takes advantage of the already established connections' history to select the best path regarding the SLA violation with the lowest cost. Simulation results verify that the proposed mechanism has better performance in terms of the blocking rate, the availability satisfaction rate, and the resource utilization than existing algorithms. Performance evaluation is done over two different simulation environments, the network with high link failure arrival rate and the network with normal link failure arrival rate. In addition to the better performance over both network topologies, the algorithm provides more revenue for service providers compared to standard and existing algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".