Evaluating Serverless Computing and Microservices Impact on Scalable Cloud-Native Applications and Blockchain Interoperability Frameworks
Bibliographic record
Abstract
The rapid evolution of cloud-native applications has intensified the demand for scalable, flexible, and cost-efficient architectures. Serverless computing and microservices have emerged as transformative paradigms, enabling dynamic resource management and modular system design for enhanced scalability. Concurrently, blockchain technology faces persistent challenges in interoperability, hindering seamless communication across diverse networks. This study evaluates the impact of serverless computing and microservices on the scalability of cloud-native applications and their role in improving blockchain interoperability frameworks. Through a systematic literature review and comparative analysis of case studies, the research examines performance metrics such as scalability, latency, and cost-efficiency. Findings reveal that serverless architectures reduce operational overhead while enabling elastic scaling, whereas microservices facilitate modular development and system resilience. Additionally, integrating these paradigms with blockchain interoperability protocols enhances cross-chain communication and transaction efficiency. The study provides practical recommendations for developers and stakeholders to optimize system designs, highlighting the potential of these technologies in advancing scalable, interoperable, and future-ready distributed systems.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".