Migrating From Legacy to Software Defined Networks: A Network Reliability Perspective
Bibliographic record
Abstract
Designing survivable communication networks to achieve carrier-grade five-nines reliability is of paramount importance for the network operators. This article addresses service reliability and its related aspects such as nodal reachability, network connectivity, and edge-disjoint routing in both traditional networks and software defined networks (SDNs). The proposed roadmap is based on two phases: Fundamental analytical phase and performance evaluation phase. In the first phase, a graph operator is defined to analyze the characteristics of the reliability metric and its associated reachability feature. This phase will focus on both the macro- and micro-level properties of reliability. In the second phase, we exploit the analysis in the former phase to get an insight into the performance evaluation of traditional and SDN-based networks against the reliability metric, and then calculate the statistical significance of the mean difference of their reliability values. Reliability under edge-disjoint paths to avoid resource competition is also investigated. Various types of topologies are utilized to test the service reliability of both architecture designs. Extensive simulation results show that SDN-based networks have comparable performance to its legacy counterpart against the operational reliability metric. Our findings not only shed light on enhancing reliability using edge-disjoint paths under link failure scenarios but also expected to benefit the operators to achieve their service level objectives while migrating from legacy to SDN-based platform.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".