SERVICE LEVEL AGREEMENT BASED ARCHITECTURES AND MECHANISMS IN PRIORITY-AWARE SHARED MESH OPTICAL NETWORKS
Bibliographic record
Abstract
Service providers’ goals include providing reliable connections with the minimum allocated resources over a shared-mesh path restoration scheme in WDM networks. However, in some cases, the requested parameters in an SLA are beyond the capacity of the network, and the connection is typically blocked. To give the customer a chance to choose another provider, or in the case of having only one provider, to comply with the provider’s network capacity, new SLA-based architectures and mechanisms are required to be introduced to provide better service to prioity-aware shared mesh WDM networks. To achieve this goal, the dissertation’s contributions focus on three main characteristics of the network design: i) A dynamic SLA negotiation infrastructure to negotiate and propagate crucial SLA parameters, ii) Path attributes which can provide a better picture of network resources and status and are suitable to be propagated by the negotiating system, and iii) Algorithms benefiting from the path attributes to improve the blocking probability and resource utilization of the network.\n\nTo fulfill the first goal of the contributions, a dynamic SLA negotiation mechanism for both intra and inter-domain communications using OSPF and BGP protocols is proposed. Link attributes via intra-domain, and new proposed TE path attributes through inter-domain mechanisms are advertised. Several novel path constraints and attributes are proposed which are dynamically updated and propagated through the network over the connections provisioning process period to satisfy the second objective of the contributions in this dissertation. The path availability, holding time, SLA violation risk, and path risk factor are the important characteristics of the proposed path attributes. As the third goal considered for the contributions, novel priority-aware algorithms and SLA-based mechanisms are proposed to improve the network performance for different traffic types of various priority classes. The algorithms and mechanisms proposed in this thesis take advantage of the new path attributes and SLA negotiation infrastructure to better serve high-priority connection requests at the lowest cost. The mechanisms and network architectures proposed in this work are a solution for the high-priority requests that normally cannot be accommodated as they violate the best availability offered by service providers.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".