On exploring proactive cloud elasticity for internet of things demands

Today, Internet of Things (IoT) is an emergent concept in which billions of devices are connected to Internet capable of producing and exchanging data. One of the most used technologies in this area regards to the Radio Frequency Identification (RFID). It can produce large amount of data from many things like objects, persons and assets. Thus, it is needed middlewares which must support processing in large scales. However, the state-of-the-art does not present satisfactory solutions in which this kind of middlewares are capable of adapt themselves according to processing demands. In this context, this article presents a proactive cloud elasticity model called Proliot aiming at providing scalability to IoT middlewares. Proliot is capable of predicting load behavior combining time series techniques. In addition, it adapts cloud resources beforehand an overload or underload situation occurs. We evaluated our model comparing results with a reactive elasticity model. In our experiments, Proliot achieved best performance up to 76% when compared to Eliot.