Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910885893636096 |
|---|---|
| author | Jung, Seungwoo Yoo, Yeonho Yang, Gyeongsik Yoo, Chuck |
| author_facet | Jung, Seungwoo Yoo, Yeonho Yang, Gyeongsik Yoo, Chuck |
| contents | Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of ~1.9%, which is highly accurate and can be applied in the real-world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15769 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations Jung, Seungwoo Yoo, Yeonho Yang, Gyeongsik Yoo, Chuck Distributed, Parallel, and Cluster Computing Machine Learning Systems and Control Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of ~1.9%, which is highly accurate and can be applied in the real-world. |
| title | Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2503.15769 |