Cost-Efficient Cluster Migration of VNFs for Service Function Chain Embedding
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
Network Function Virtualization (NFV) is a network architecture that separates network functions from dedicated hardware, implementing them as software modules known as Virtual Network Functions (VNFs), which are executed in virtual machines or containers. NFV increases the deployment flexibility and agility within operator networks and reduces the operating and capital expenditures significantly. In NFV, migration of VNFs can significantly reduce the embedding cost. However, stringent latency requirements between VNFs can make them tightly coupled, thus hindering each VNF from being migrated individually, and resulting in poor performance. One of the main challenges in an NFV environment is therefore to migrate a cluster of VNFs to minimize the embedding cost. In this paper, we aim to solve the problem of cluster VNF migration by considering the given inter-VNF latency requirements. We formulate the VNF migration problem as an Integer Linear Programming (ILP) and present two scalable and efficient algorithms for migrating a cluster of VNFs. Through extensive experiments, we show that our proposed algorithms are highly effective. They reduce the total embedding cost by 14% compared to the existing heuristics, while being much more scalable in terms of execution time compared to the brute-force approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it