As cloud computing and the Internet of Things have taken hold of corporate imaginations, there is an underlying assumption that they will bring about a lot of devices feeding raw data to centralized enterprise or cloud-based data centers for storage and analysis. Thus, the concerns have been around how quickly enterprises and cloud companies can build or rent massive amounts of horsepower and storage to handle these growing workloads.
A new consortium, however, thinks things will fall out differently. Rather than devices generating tons of data that are sent to centralized locations, the data will be handled and applications run on the network somewhere, in a highly distributed manner -- as "fog computing," which may be anywhere along the spectrum of centralized data centers, edge of network, or somewhere in between.
Fog computing technology distributes the resources and services of computation, communication, control, and storage closer to devices and systems at or near the users. A fog computing architecture performs analytics or run applications on anything from the network center to the edge -- wherever it makes the most sense, economically and technically .
The notion of fog computing was first promulgated by Cisco, which, as a network provider, would have a natural stake in seeing more power going to the network. Now, Cisco has been joined by several other companies -- as well as an Ivy-League university -- in promoting the idea of fog computing.
The OpenFog Consortium was launched in November by Cisco, along with ARM, Dell, Intel, Microsoft Corp., and the Princeton University Edge Laboratory. The stated goal of the consortium is to accelerate the deployment of "fog" technologies, and is mainly geared toward facilitating the growth and promise of IoT. The founding members will build initial frameworks and architectures that reduce the time required to deliver the end-to-end IoT scenarios.
Additional organizations lending support to the consortium since the inital launch include Arizona State University, FogHorn Systems, Fujitsu, GE Digital, Georgia State University, IEEE, MARSEC Inc., National Chiao Tung University, Nebbiolo Technologies, PrismTech, Real-Time Innovations, Schneider Electric, and Toshiba.
As part of my work with RTInsights.