Multiple Optimization of Network Carrier and Traffic Flow Goals Using a Heuristic Routing Decision System
Bibliographic record
Abstract
This chapter presents a multiple constraint optimization algorithm called routing decision system (RDS) that uses the concept of preference functions to address the problem of selecting paths in core networks that satisfy traffic-oriented QoS requirements while simultaneously satisfying network resource-oriented performance goals. The original contribution lies in the use of strong scales employed for constructing a multiple criteria preference function in an affine space. The use of preference functions makes it possible for paths that match both traffic-oriented and resource-oriented goals to be selected by the algorithm. The RDS algorithm is used in conjunction with a heuristic path finding algorithm called Constraint Path Heuristic (CP-H) algorithm which is a novel approach to finding a set of constraint paths between source and destination nodes in a network. The CP-H algorithm finds multiple paths for each metric and then passes all the paths to the RDS algorithm. Simulation results showed that the CP-H/RDS algorithm has a success rate of between 93 and 96% when used in Waxman graph topologies, and is shown to be significantly better than other heuristic based algorithms under strict constraints. In addition, it is shown that the associated execution time of the CP-H/RDS algorithm is slightly higher than other heuristic based algorithms but good enough for use in an online traffic engineering (TE) application. Simulations to assess the performance of CP-H/RDS algorithm in a TE environment show that the algorithms has lower call block rates than other TE algorithms. It is also shown that the CP-H/RDS has a 96% probability of providing at least two distinct feasible backup paths in addition to the main QoS path. A framework for implementing the CP-H/RDS as a routing server is proposed. The routing decision system server (RDSS) framework is novel in that the complexity introduced by QoS awareness remains outside the network.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".