Liability and Trust Analysis Framework for Multi-Actor Dynamic Microservices
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| Titel: | Liability and Trust Analysis Framework for Multi-Actor Dynamic Microservices |
|---|---|
| Autoren: | Yacine Anser, Chrystel Gaber, Jean-Philippe Wary, Samia Bouzefrane, Méziane Yacoub, Onur Kalinagac, Gürkan Gür |
| Weitere Verfasser: | Bertram, Marie-Liesse |
| Quelle: | IEEE Transactions on Network and Service Management. 22:58-71 |
| Verlagsinformationen: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Market research, Measurement, Monitoring, Machine Learning (ML), Service Level Agreement (SLA), Machine learning (ML), 005: Computerprogrammierung, Programme und Daten, Vectors, [INFO] Computer Science [cs], Trust, Microservice, Microservice architectures, Service level agreement (SLA), Microservices, Liability, Computer architecture, Service level agreements |
| Beschreibung: | Microservices architecture has become an increasingly common approach for building complex software systems. With the distributed nature of microservices, multiple actors can contribute to a service, hence affecting the dynamics of the environment and making the management of liabilities and trust more challenging. Service-Level Agreements (SLAs) are critical in that regard and any SLA violation or breach can result in significant financial damages. One major challenge is the lack of indicators to handle the liability and trust in such architectures. To address this issue, in this paper we propose a liability and trust analysis framework, namely the LASM Analysis Service (LAS), for multi-actor dynamic microservices that employs Machine Learning (ML) techniques. |
| Publikationsart: | Article |
| Dateibeschreibung: | application/pdf |
| ISSN: | 2373-7379 |
| DOI: | 10.1109/tnsm.2024.3417934 |
| DOI: | 10.21256/zhaw-32449 |
| Zugangs-URL: | https://cnam.hal.science/hal-04642127v1 https://cnam.hal.science/hal-04642127v1/document https://doi.org/10.1109/tnsm.2024.3417934 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....f13a95b1ca4cc2cbe8017a670574a0cc |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Microservices architecture has become an increasingly common approach for building complex software systems. With the distributed nature of microservices, multiple actors can contribute to a service, hence affecting the dynamics of the environment and making the management of liabilities and trust more challenging. Service-Level Agreements (SLAs) are critical in that regard and any SLA violation or breach can result in significant financial damages. One major challenge is the lack of indicators to handle the liability and trust in such architectures. To address this issue, in this paper we propose a liability and trust analysis framework, namely the LASM Analysis Service (LAS), for multi-actor dynamic microservices that employs Machine Learning (ML) techniques. |
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| ISSN: | 23737379 |
| DOI: | 10.1109/tnsm.2024.3417934 |