Joint Container Placement and Task Provisioning in Dynamic Fog Computing
Why this work is in the frame
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Bibliographic record
Abstract
Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this article, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with integer linear programming. Due to its NP-hardness, we propose a low-complex particle-swarm-optimization-based metaheuristic and a greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior/mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources' availability models, conducted simulations demonstrate that the PSO-based algorithm achieves near-optimal results. Whereas, the greedy algorithm realizes only 10%-30% less success ratio than the optimal solution with negligible execution time.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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