Joint VNF Placement and Scheduling for Latency-Sensitive Services
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
Next-generation 6G networks are envisioned to be a key enabler for low-latency services (e.g., extended reality, remote surgery), which cannot be potentially realized by currently deployed networks. Network function virtualization (NFV) and software-defined networking (SDN) are going to continue playing their key role as two promising technologies in 6G to realize these services due to flexibility, agility, scalability, and cost-efficiency. Although NFV and SDN bring several benefits, provisioning latency-sensitive network services (NSs) in an NFV-based infrastructure remains a challenge, as they require stringent service deadlines. To efficiently meet such stringent service deadlines, VNF placement and scheduling need to be carried out jointly. Most of the existing studies tackle these two problems separately. In this paper, we study the joint VNF placement and scheduling problem for latency-sensitive NSs. We aim at optimally determining whether to place new VNFs or to reuse the already deployed VNFs to optimize profits while guaranteeing stringent deadlines. To solve the problem, we formulate it as an integer linear programming (ILP) problem. Due to its complexity, we also propose two efficient heuristics, namely, greedy-based and Tabu search-based algorithms to solve the problem. The simulation results show that our proposed algorithms achieve higher profits than the existing benchmarks.
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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 | 0.001 | 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 | 0.001 | 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