Adaptive Semantic-Aware Traffic Management in ASP.Net Core: A Contextual Framework for Dynamic Routing and Risk-Based Request Prioritization
Bibliographic record
Abstract
In modern web applications, dynamic traffic shaping based on user context is essential for optimizing performance, enhancing security, and delivering personalized experiences. This thesis proposes a semantic-aware traffic management framework in ASP.NET Core that leverages contextual metadata—such as geolocation, device type, user roles, and historical session behavior—to inform adaptive routing decisions at runtime. By integrating custom middleware, OpenTelemetry-based observability, and policy-driven routing mechanisms, the system dynamically adjusts request flows across distributed microservices. An embedded risk evaluation engine assesses incoming requests using metadata and behavioral heuristics, triggering route prioritization or reallocation based on perceived threat levels or operational load. Semantic tagging enriches request headers, enabling more granular control and intelligent filtering within the reverse proxy layer powered by YARP. The architecture supports scalable deployment on containerized environments using Kubernetes and Azure App Gateway for high availability and traffic governance. Comprehensive testing demonstrates measurable improvements in response time, system resilience, and threat mitigation. This work contributes a robust and extensible approach to context-aware traffic orchestration within enterprise-grade .NET ecosystems, aligning with evolving demands for adaptive, secure, and responsive web infrastructures
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 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".