Dynamic Flow Migration for Embedded Services in SDN/NFV-Enabled 5G Core Networks
Bibliographic record
Abstract
Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".