Preemptive Prediction-Based Placement of Time-Critical SFCs With VNF Sharing at the Edge
Bibliographic record
Abstract
The demand for ultra-low latency requirements is fueled by the growing popularity of real-time and time-critical applications such as virtual, augmented, and mixed reality, and industrial IoT. Time-critical applications and services are real-time software whose failure could result in catastrophic consequences such as fatalities, damage to property, and even financial losses. Edge computing is the main enabler of 5G ultra-low latency use cases. Edge resources are limited compared to abundant cloud computing resources. As such, provisioning time-critical applications at the edge is more challenging and demanding. Even though virtual network function (VNF) sharing improves the utilization of the service providers’ resources, service requests, including time-critical ones, can still be rejected due to insufficient resources. This paper proposes a Preemptive Prediction-based Placement scheme (PPPS) for time-critical services with VNF sharing. In addition to prioritizing time-critical premium (Pr) services over best-effort (BE) services, PPPS utilizes the predicted required resources in a defined lookahead window. In cases when no resources are available for Pr services, a preemption mechanism preempts resources for the Pr service, by deporting one or more running BE services. The experimental results show that PPPS can reduce the Pr services rejection rate to ~0% while minimizing the disturbance that BE services witness such as prolonged waiting times.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".