Using DEVS for modeling and simulating a Fog Computing environment
Bibliographic record
Abstract
With the increase in popularity of Internet of Things (IoT), pervasive computing, healthcare services, sensor networks, and mobile devices, a lot of data is being generated at the perception layer. Cloud is the most viable solution for data storage, processing, and management. Cloud also helps in the creation of further services, refined according to the context and requirement. However, being reachable through the Internet, cloud is not efficient enough for latency sensitive multimedia services and other time-sensitive services, like emergency and healthcare. Fog, an extended cloud lying within the proximity of underlying nodes, can mitigate the issues traditional cloud cannot solve being standalone. Fog can provide quick response to the requiring applications. Moreover, it can preprocess and filter data according to the requirements. Trimmed data is then sent to the cloud for further analysis and enhanced service provisioning. However, how much better is it to have a fog in any particular scenario instead of a standalone cloud working without fog is a question right now. In this paper, we provide an answer by analyzing both cloud-only and cloud-fog scenarios in the context of processing delay and power consumption according to increasing number of users, on the basis of varying server load. The simulation is done through Discrete Event System Specification (DEVS). Simulation results demonstrate that by the use of fog networks, users experienced lower waiting times and increased data rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".