Substrate network house cleaning via live virtual network migration
Bibliographic record
Abstract
Network virtualization techniques aim at efficiently allocating the underlying substrate network (SN) resources to the hosted virtual networks (VNs). Unfortunately, over time, and due to the frequent initiation and termination of VNs, the available and utilized SN resources become fragmented. This in turn, gradually degrades the performance of these techniques. In this paper, we propose a novel proactive SN resource re-optimization technique that efficiently overcomes the fragmentation problem by performing appropriate re-arrangement, or house cleaning, for the available and utilized SN resources. To minimize the incurred computational overhead, the invocation of this technique is only triggered by certain events such as the departure of an expired VN. The contributions of the proposed work are two fold; first, we develop an efficient technique for the selection and re-allocation of VN portions that are contributing to the fragmentation problem. The technique takes into consideration the trade-off between the benefit from increasing the SN utilization and the cost incurred by the VN migration. The second contribution is novel VN live migration techniques that significantly reduce the service interruption time during migration. Simulation experiments demonstrate the achieved gain in the SN resource utilization as well as in the VN acceptance ratio and the net revenue.
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.
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 | 0.000 | 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 | 0.000 | 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".