SmartEdge: fog computing cloud extensions to support latency-sensitive IoT applications
The rapid growth in the number of Internet-connected devices, associated to the increasing rates in popularity and demand for real-time and latency-constrained cloud application services makes the use of traditional cloud computing frameworks challenging to afford such environment. More specifica...
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| Formato: | Dissertação |
| Idioma: | por |
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Brasil
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/22557 |
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| Resumo: | The rapid growth in the number of Internet-connected devices, associated to the increasing
rates in popularity and demand for real-time and latency-constrained cloud application services
makes the use of traditional cloud computing frameworks challenging to afford such environment.
More specifically, the centralized approach traditionally adopted by current Data Center
(DC) pose performance issues to suit a high density of cloud applications, mainly in terms to
responsiveness and scalability. Our irreplaceable dependency on cloud computing, demands DC
infrastructures always available while keeping, at the same time, enough performance capabilities
for responding to a huge amount of cloud application requests. In this work, the applicability
of the fog computing emerging paradigm is exploited to enhance the performance on supporting
latency-sensitive cloud applications tailored for Internet of Things (IoT).With this goal in mind,
we introduce a new service model named Edge Infrastructure as a Service (EIaaS), which seeks
to offer a new edge computing tailored cloud computing service delivery model to efficiently
suit the requirements of the real-time latency-sensitive IoT applications. With EIaaS approach,
cloud providers are allowed to dynamically deploy IoT applications/services in the edge computing
infrastructures and manage cloud/network resources at the run time, as means to keep
IoT applications always best connected and best served. The resulting approach is modeled in a
modular architecture, leveraging both container and Software-Defined Networking technologies
to handle edge computing (CPU, memory, etc) and network resources (path, bandwidth, etc) respectively.
Preliminary results show how the virtualization technique affects the performance of applications at the network edge infra. The container-based virtualization takes advantage over the hypervisor-based technique for deploying applications at the edge computing infrastructure, as it offers a great deal of flexibility under the presence of resource constraints. |
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