Blockchain and smart contracts for telecommunications: requirements vs. cost analysis
Blockchain technology offers solutions to numerous network problems by leveraging distributed record-keeping and collaborative decision-making features. However, deployment considerations such as blockchain infrastructure cost, performance requirements, and scalability are often overlooked. This pap...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/71529 |
| Acceso en línea: | http://hdl.handle.net/10230/71529 http://dx.doi.org/10.1109/ACCESS.2023.3309423 |
| Access Level: | acceso abierto |
| Palabra clave: | 5G network slicing Blockchain for telecom Blockchained federated learning Blockchain scalability Cloud-native distributed ledger Cost analysis Permissioned blockchain Smart contracts |
| Sumario: | Blockchain technology offers solutions to numerous network problems by leveraging distributed record-keeping and collaborative decision-making features. However, deployment considerations such as blockchain infrastructure cost, performance requirements, and scalability are often overlooked. This paper provides an in-depth perspective on deploying blockchain-based solutions for telecommunications networks, estimating costs, comparing infrastructure options (on-premises, IaaS, BaaS), and choosing a suitable blockchain platform. To that end, we identify the performance limitations of the proposed solution under various deployment infrastructures by studying two prominent use cases: one proposing a distributed marketplace solution for 5G slice brokering and another one on the decentralization of federated learning (FL) through blockchain. For the slice brokering use case, our experiments showed that sub-second latency could be achieved for a maximum transaction throughput in the range of 10 to 200 transactions per second (TPS), whereas use cases requiring a higher throughput (300 to 400 TPS) would need more computational resources. Meanwhile, the FL use case provided insights into the achievable accuracy of distributed learning under various blockchain settings (public, consortium, and private), which led to the understanding that private and consortium blockchains can achieve acceptable accuracy in significantly lower training times compared to public blockchains. |
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