Sustainable expert virtual machine migration in dynamic clouds
Operation on demand flexibility in cloud computing services has resulted in great popularity and wide adoption. These services integrate thousands of computers, storage and communication networks, which implies a high consumption of electrical energy. Therefore, renewable energy-based cloud data cen...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/4048 |
| Acceso en línea: | https://doi.org/10.1016/j.compeleceng.2022.108257 https://www.sciencedirect.com/science/article/pii/S004579062200489X https://hdl.handle.net/10953/4048 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud computing Migration of virtual machines Scheduling Follow the renewable Expert systems |
| Sumario: | Operation on demand flexibility in cloud computing services has resulted in great popularity and wide adoption. These services integrate thousands of computers, storage and communication networks, which implies a high consumption of electrical energy. Therefore, renewable energy-based cloud data centers are replacing traditional energy power grids. In this regard, the workload could be transferred to different nodes among different cloud data centers geographically distributed regarding renewable energy availability. In this sense, this paper presents a framework based on Cloudsim for virtual machine migrations among cloud data centers regarding sustainability optimization. Moreover, an approach for migrations among datacenters based on an expert system has been tested in several scenarios with renewable energy dynamically available. Experimental results show the improvements of the proposed framework and how expert systems can take advantage of renewable energy availability in terms of sustainability while preserving the QoS in terms of execution time. |
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