Integrated cloud computing and blockchain systems: A review
Bibliographic record
Abstract
Blockchain technology is one of the crypto-currency technologies that has received a lot of attention. It has also found use in various applications, including the Internet of Things (IoT) and Cloud computing. Nonetheless, Blockchain has a significant scalability issue, restricting its ability to support services with various transactions. On the other hand, cloud computing is the on-demand availability of shared computer system resources, although issues now beset it in automation, processes, management, policies, and human aspects. Combining cloud computing and blockchain technology into a single system can improve network control, task scheduling, data integrity, resource management, pricing, fair payment, and resource allocation. In this article, we offered a comprehensive and up-to-date survey of cloud computing and Blockchain integration, a critical service for business applications due to the benefits of privacy, security, and service support. The lack of a comprehensive assessment examining the significance of BaaS platforms used in cloud computing prompted this review. We focus on the various BaaS tools that are currently in use. This report also examines the most common BaaS platforms incorporating Blockchain as a cloud service, such as Alibaba, Oracle, Azure, Amazon, and IBM. Furthermore, this research highlighted some major technological issues associated with merging Blockchain with cloud computing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".