A Scalable Approach for Service Chain Mapping With Multiple SC Instances in a Wide-Area Network
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Bibliographic record
Abstract
Network function virtualization (NFV) aims to simplify service deployment using virtual network functions (VNFs). Service deployment involves the placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a service chain (SC). The joint VNF placement and traffic routing is called SC mapping. In a wide-area network (WAN), where several traffic flows, generated by many distributed node pairs, require the same SC; a single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for same SC is a very complex problem, since sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. This paper is the first to deal with the problem of SC mapping with multiple SC instances to minimize network resource consumption. We propose an integer linear program (ILP), a column-generation-based ILP (CG-ILP), and a two-phase column-generation-based model (2PhMod) to solve this problem. ILP does not scale to large networks and CG-ILP scalability is limited by quadratic constraints. So, to get results over large network topologies within reasonable computational times, we propose 2PhMod. Using such an approach, we observe that an appropriate choice of only a small set of SC instances leads to a solution very close to minimum bandwidth consumption. Furthermore, this approach also helps us to analyze effects of number of VNF replicas and number of NFV nodes on bandwidth consumption when deploying these minimum number of SC instances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it