| Texto completo | |
| Autor(es): |
Carvalho, Marcos
;
Macedo, Daniel Fernandes
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT; v. 20, n. 3, p. 14-pg., 2023-09-01. |
| Resumo | |
Existing Cloud deployments usually perform automated scheduling and rescheduling based on Quality of Service (QoS) objectives. Services are migrating towards Quality of Experience (QoE), which maps the user experience more effectively than QoS. This work proposes extensions to the Kubernetes scheduler in order to employ QoE objectives into the algorithm. For that, we created deep learning models (using LSTM) to estimate user's QoE that the cloud can offer. The evaluation was performed on a testbed, and considered two QoE-aware applications (live classroom and video on demand). Experimental results in a testbed show that our scheduler improves the average QoE by at least 61.5% compared to other schedulers, while our proposed resource rescheduling improved the QoE by up to 119%, keeping the average QoE closer to the maximum. (AU) | |
| Processo FAPESP: | 18/23097-3 - SFI2: fatiamento de infraestruturas de internet do futuro |
| Beneficiário: | Tereza Cristina Melo de Brito Carvalho |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |