Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments
Bibliographic record
Abstract
The "Internet of Things" describes a network comprising variedly distributed and heterogeneous devices that communicate by exchanging data to realize various applications with minimal human intervention.However, processing the massive amounts of data in the cloud environment becomes challenging.To address this issue, fog computing has appeared as a new paradigm that extends the capabilities of cloud computing to the edge of networks.The deployment of applications on diverse and dispersed nodes is one of the key issues in fog computing This article presents an approach to optimize application placement in fog computing infrastructure by formulating it as a combinatorial problem that aims to minimize both execution times and costs.Here, we propose a Discrete Black Widow Optimization (DBWO) algorithm specifically designed to tackle the discrete nature of the application placement in fog environments.Experimental results show that our approach demonstrates an average improvement of 9% compared to several recent approaches in the literature.In fog-only topology, DBWO demonstrated an improvement range from 4.30% to 9.87%, while in fog-cloud topology, it showed notable performance improvement, with fitness value enhancement ranging from 8% to 15.16%.This innovation represents a significant stride towards efficient and cost-effective application placement in fog computing environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".