Design of Resource‐Aware Load Allocation for Heterogeneous Fog Computing Environments
Bibliographic record
Abstract
The architecture employed by most of the researchers for the deployment of latency‐sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end‐to‐end delay.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".