Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1324
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dc.contributor.authorSiriwardhana, G.S.-
dc.contributor.authorDe Silva, N.-
dc.contributor.authorJayasinghe, L.S.-
dc.contributor.authorVithanage, L.-
dc.contributor.authorKasthurirathna, D.-
dc.date.accessioned2022-02-21T10:58:17Z-
dc.date.available2022-02-21T10:58:17Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1324-
dc.description.abstractIn today's world of software application development, Kubernetes has emerged as one of the most effective microservice deployment technologies presently available due to its exceptional ability to deploy and orchestrate containerized microservices. However, a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments, which consequently inhibit the creation of more optimized deployment policies. In response to this issue, this publication proposes the use of a network science-based approach to facilitate the creation of a microservice governance model that incorporates the use of dependency analysis, load prediction, centrality analysis, and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment. Furthermore, through analysis of the factors mentioned above, the research conducted, then proceeds to create an optimized deployment strategy for the deployment with the aid of a developed optimization algorithm. Analysis of results revealed the developed governance model aided through the utilization of the developed optimization algorithm proposed in this publication, proved to be quite effective in the generation of optimized microservice deployment policies.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.subjectAuto-scalingen_US
dc.subjectChaos Engineeringen_US
dc.subjectKubernetesen_US
dc.subjectMachine Learningen_US
dc.subjectMicroservicesen_US
dc.subjectNSGA-IIen_US
dc.subjectTime Seriesen_US
dc.titleA Network Science-Based Approach for an Optimal Microservice Governanceen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357232en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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