Читать книгу Fog Computing - Группа авторов - Страница 84

The never-ending increase in interconnected IoT devices and the stringent requirements of new IoT applications has posed severe challenges to the current cloud computing state-of-the-art architecture, such as network congestion and privacy of data. As a result, researchers have proposed a new solution to tackle these challenges by migrating some computational resources closer to the user. The approach taken in this solution made the cloud more efficient by extending its computational capabilities at the end of the network, solving its challenges in the process.

Continuing to improve this solution, multiple paradigms appeared, having as their underlying vision the same goal of deploying more resources at the edge of the network. Besides their common vision, some paradigms were influenced by their considered use case, e.g. MEC paradigm enables constrained devices like smartphones to offload parts of the applications to save resources. However, two of the most popular paradigms (i.e. fog and edge computing) are widely used in research today.

These two paradigms were designed to enable processing IoT applications at the endpoints of the network, sharing more similarities than others. Other than the naming convention, the difference at the beginning for the two, i.e. fog computing extends the cloud creating a cloud-to-things continuum and edge computing places the application directly on the edge devices, was represented by the location where computations are performed. Since in the past couple of years there were tremendous advances for edge devices, this difference between the two has disappeared, both fog and edge aiming to deploy applications as close as possible to the edge of the network. Considering the similarities they share, we argue that there is no difference between their purpose of them.