Authors:
Guilherme Gabriel Barth
;
Rodrigo da Rosa Righi
;
Cristiano André da Costa
and
Vinicius Facco Rodrigues
Affiliation:
Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
Keyword(s):
Fog Computing, IoT, Resource Management, Elasticity, Prediction.
Abstract:
Today, streaming, Artificial Intelligence, and the Internet of Things (IoT) are being some of the main drivers to accelerate process automation in various companies. These technologies are often connected to critical tasks, requiring reliable and scalable environments. Although Fog Computing has been on the rise as an alternative to address those challenges, we perceive a gap in the literature related to adaptability on the number of resources on both cloud and fog layers. Multiple studies suggest different Cloud-Fog architectures for IoT implementations, but not thoroughly addressing elasticity control mechanisms. In this context, this article presents ProFog as a proactive elasticity model for IoT-based Cloud-Fog architectures. ProFog uses the ARIMA prediction model to anticipate load behaviors, so triggering scaling actions as close to when they are required as possible. This strategy allows the delivery of new resources before reaching an overloaded or underloaded state, benefiti
ng performance, and energy saving. We developed a ProFog prototype that showed an improvement of 11.21% in energy consumption in favor of ProFog.
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