Autonomic traffic engineering for network robustness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The continuously increasing complexity of communication networks and the increasing diversity and unpredictability of traffic demand has led to a consensus view that the automation of the management process is inevitable. Currently, network and service management techniques are mostly manual, requiring human intervention, and leading to slow response times, high costs, and customer dissatisfaction. In this paper we present AutoNet, a self-organizing management system for core networks where robustness to environmental changes, namely traffic shifts, topology changes, and community of interest is viewed as critical. A framework to design robust control strategies for autonomic networks is proposed. The requirements of the network are translated to graph-theoretic metrics and the management system attempts to automatically evolve to a stable and robust control point by optimizing these metrics. The management approach is inspired by ideas from evolutionary science where a metric, network criticality, measures the survival value or robustness of a particular network configuration. In our system, network criticality is a measure of the robustness of the network to environmental changes. The control system is designed to direct the evolution of the system state in the direction of increasing robustness. As an application of our framework, we propose a traffic engineering method in which different paths are ranked based on their robustness measure, and the best path is selected to route the flow. The choice of the path is in the direction of preserving the robustness of the network to the unforeseen changes in topology and traffic demands. Furthermore, we develop a method for capacity assignment to optimize the robustness of the network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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