ENSC: Multi-Resource Hybrid Scaling for Elastic Network Service Chain in Clouds
Bibliographic record
Abstract
Software-based network service chains in Network Function Virtualization (NFV) need to be dynamically allocated and scaled on hardware resources. This is because the resource demand of virtual network functions (VNFs) typically varies as a results of network flow volume. NFV elastic solutions by coarse-grained horizontal scaling or fine-grained vertical scaling have been investigated in recent years. However, none of the existing solutions can achieve both efficiency and scalability. To address this challenge, we propose elastic network service chain (ENSC), which utilizes a fine-grained hybrid scaling method to achieve both NFV efficiency and scalability. We systematically compare horizontal scaling with vertical scaling from six aspects and determine the priority within hybrid scaling. We formulate the resource allocation problem in the cloud datacenter as an integer linear programming (ILP) model and develop a heuristic algorithm called Rubik. Our evaluation results show that ENSC achieves higher acceptance ratios and resource utilization than horizontal scaling and vertical scaling methods.
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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 | 0.001 | 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 | 0.001 | 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 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".