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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.08551 |
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| _version_ | 1866915504440999936 |
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| author | Ren, Zhiyuan Jian, Xinke Cheng, Wenchi Yang, Kun |
| author_facet | Ren, Zhiyuan Jian, Xinke Cheng, Wenchi Yang, Kun |
| contents | Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path's information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust "stable belts" and high-risk "dangerous wedges". Finally, an analysis of the landscape's curvature yields actionable, topology-aware design rules, including an optimal "Threshold-First" tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity Ren, Zhiyuan Jian, Xinke Cheng, Wenchi Yang, Kun Information Theory Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path's information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust "stable belts" and high-risk "dangerous wedges". Finally, an analysis of the landscape's curvature yields actionable, topology-aware design rules, including an optimal "Threshold-First" tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks. |
| title | The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.08551 |