Sphere: Simulator of edge infrastructures for the optimization of performance and resources energy consumption
Edge computing constitutes a key paradigm to address the new requirements of areas such as smart cars, industry 4.0, and health care, where massive amounts of heterogeneous data from continuous geographically-distributed sources have to be processed and computed near real-time. To this end, new dist...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/130751 |
| Acceso en línea: | https://hdl.handle.net/11441/130751 https://doi.org/10.1016/j.simpat.2019.101966 |
| Access Level: | acceso abierto |
| Palabra clave: | Edge computing Fog computing Cloudlet computing Cloud computing Energy-aware scheduling |
| Sumario: | Edge computing constitutes a key paradigm to address the new requirements of areas such as smart cars, industry 4.0, and health care, where massive amounts of heterogeneous data from continuous geographically-distributed sources have to be processed and computed near real-time. To this end, new distributed infrastructures consisting on small computing clusters close to data sources, also known as Cloudlets have emerged. In order to evaluate the performance of these solutions we present Sphere, a simulation tool that enables researchers to establish various scenarios, including: (a) topology and orchestration model of the infrastructure; (b) incoming workload patterns; (c) resource-managing models; and (d) scheduling policies. Moreover, Sphere allows researchers to apply efficiency and performance policies both at infrastructure and cluster levels. The simulator presents the following benefits: (a) Evaluation of various orchestration models; (b) Analysis of resource-efficiency and performance strategies at Edge-infrastructure and cluster (Cloudlet/Cloud) level; (c) Execution of diverse workload generation patterns; (d) Evaluation of strategies for the infrastructure communication, as well as the impact on tasks completion time (makespan); and (e) Simulation of each cluster (Cloudlet/Cloud) independently, including resource-managing, scheduling and resource-efficiency models. Finally, we performed a deep evaluation based on realistic Edge-Computing use cases. The results of the experiments confirm that it is a performant and reliable tool for the analysis of orchestration, graph-resolving, energy-efficiency, resource-managing and scheduling strategies in Edge-computing environments. |
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