The journey to cloud as a continuum: Opportunities, challenges, and research directions
Bibliographic record
Abstract
The rapid development of the Internet of Things (IoT) has driven a significant shift in computing architectures, leading to the rise of the cloud continuum—a flexible framework that combines cloud services with edge and fog computing. While existing survey papers have contributed valuable insights, they often focus narrowly on specific aspects of the continuum or do not fully address its evolving complexities. These limitations underscore the need for a comprehensive and up-to-date analysis of the field. This study bridges these gaps by presenting an extensive review of the cloud continuum, covering its role in enhancing resource management, improving real-time data processing, integrating machine learning approaches, and optimizing user experiences across diverse applications. We examine how edge devices, fog nodes, and cloud infrastructures synergize to enable decentralized data processing, reducing latency in critical areas such as smart cities, healthcare, and autonomous vehicles. Additionally, this study explores the integration of machine learning across edge, fog, and cloud layers, with a focus on inference and distributed learning methods. By highlighting how these technologies enhance efficiency, scalability, and intelligent decision-making, this review provides a holistic perspective on the cloud continuum. Our analysis offers valuable insights into future research directions, emphasizing innovations that can drive next-generation computing systems toward greater efficiency and adaptability.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".