Since fog computing’s unique infrastructure has communication network devices scattered everywhere, mobile device users can expect to see better and faster connections to a multitude of mobile applications.
The trick is managing resources and storing data while mobile users are on the go.
This means designing schedules and algorithms that make the best use of nearby devices for processing and storing data.
“With increasing focus on Internet-of-Things (IoT), countless devices scattered and connected to the Internet, producing and consuming data requires scalable resource management at unprecedented levels. The data dynamism and heterogeneity resulting from this expected explosive expansion of connected devices, commonly referred in a broad sense as Big Data, also requires new processing models and infrastructures to support its main dimensions: data volume, velocity, and variety,” writes lead author Luiz F. Bittencourt, assistant professor at the University of Campinas (UNICAMP), Brazil.
“One key aspect of this new era is that both data consumption and production are heavily distributed and at the edges of the network (i.e. closer to or at end-user devices). While the centralized data center model of cloud computing can cope with many types of applications and large amounts of data, its infrastructure and network
connection to the edge are not designed to handle this Big Data phenomenon. In this context, computing and data management models that support computing capacity at the edges of the network are now a focus of significant research. Mobile clouds, vehicular networks, and fog computing are examples of new distributed computing models that leverage edge capacity closer to data production,” Bittencourt and the other authors write.
In the March/April 2017 issue of IEEE Cloud Computing, two application models that researchers are using to improve fog infrastructure include an EEG tractor beam game in which players try to move objects with their brain waves, and a video surveillance/object tracking application used, for example, to identify people on multiple cameras in a city as they move from one place to another.
The applications are ideal because one requires real-time game play while the other one is more delay-tolerant.