A Dynamic Priority Service Provision Scheme for Delay-Sensitive Applications in Fog Computing
Bibliographic record
Abstract
The massive numbers of connected devices in the IoT era impose a real challenge in the management of both communication and computing resources. Moreover, the competition of those devices on the limited resources will inherently raise the delay experienced by users. To this end, we propose a priority service provision scheme to reduce the latency experienced by delay-sensitive services. Here, incoming tasks are classified into delay-sensitive and delay-insensitive whereby priority classes are assigned using a matching theory approach. Then, the queue delay experienced by each class is investigated at the computing node i.e., the edge device, and the communication node i.e., the small base station (SBS). To maintain high quality of experience (QoE) in regard with time delay for all tasks, a dynamic priority scheme is proposed and controlled using a heuristic algorithm. The goal of the dynamic priority scheme is to minimize the delay at the communication node (SBS) for users requesting non-computing tasks (e.g., regular phone calls) by promoting their class when the delay exceeds a threshold value. The combined delay experienced at both communication and computing nodes is compared using the prioritized, non-prioritized, and the dynamic priority schemes. Results show that undertaking a dynamic priority service provision can achieve significant reduction in the amount of delay experienced by users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".