Optimized Synchronization of the Orchestrator In Hierarchical Multi-Layer Networks
Bibliographic record
Abstract
The orchestrator coordinates multiple layers, such as IP, OTN, and DWDM, in a Multi-Layer Network (MLN), in which each layer is a domain. Accurately updating the orchestrator's network view is a key challenge in MLNs. Due to the hierarchical structure of MLNs, a single failure in an underlying layer can propagate to the upper layers, hence generating many alarms in each of these layers. These alarms may send incorrect updates of the root cause of the failure, which results in incorrect decisions of the orchestrator for routing and resource allocation. Therefore, setting up the order of updating the orchestrator by different layers is crucial in MLN to avoid confusion in the orchestrator's network view. This task is challenging, due to the flexible mapping of links between different layers, and also to the failure propagation time from the underlying layers to the upper layers. In this paper, we propose a method to update the orchestrator to ensure that the root cause is reported correctly, taking into account the dependency among different layers and failure propagation time. Moreover, to compute the optimal frequency of sending update messages from layers to the orchestrator. Our proposed method can be implemented in the Topology Server (TS) of the orchestrator. We formulate an integer nonlinear optimization problem for updating the orchestrator and then propose an algorithm to approximate the optimal failure probability for updating the orchestrator. Simulation results show that our algorithm can obtain a near-optimal which is, on average 11.3% different from the global minimum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".