Two Efficient QoS-Based Approaches for a Resource Splitting Strategy across Multiple Cloud Providers
Bibliographic record
Abstract
In this paper, we address the problem of com-putational and networking virtual resources embedding across multiple Infrastructure-as-a-Service (IaaS) providers. This issue, usually referred to as the Virtual Network Embedding (VNE) problem, requires two phases of operation in such a context: the multicloud virtual network requests (VNRs) splitting, followed by the intracloud VNR segments mapping. This paper focuses on the splitting phase problem, by proposing a splitting strategy based on two optimization approaches, with the objective of improving the performance and the quality of service (QoS) of resulting mapped VNR segments. An Integer Linear Program (ILP) is used to formalize our splitting strategy as a mathematical minimization problem with constraints. The ILP model is first solved with the exact approach. Subsequently, a metaheuristic approach based on the Tabu Search (TS) is proposed in order to find optimal or near-optimal solutions in polynomial solving time. The simulation results obtained show the efficiency of the proposed VNRs splitting approaches according to several performance criteria. Solution costs of the heuristic are on average close to the exact solution, with an average cost gap ranging from 0% to a maximum of 2.05%, performed in a highly reduced computing time. In comparison with other baseline approaches, the acceptance rate and the delay are improved by approximately 15%, while preventing QoS violations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".