Fog computing is an architecture that uses one or more of edge devices to carry out:
1. a substantial amount of storage (instead of storing data in cloud),
2. communication (instead of routing over internet),
3. control, configuration and management (instead of being controlled by network gateways).
It is also referred to as “Fog networking” or “Fogging”.
The effects of fog computing on cloud computing and big data systems in common is to resolve the limitation in accurate content distribution. It is especially of significance for the numerous connected sensors in internet of things (IoT). For instance, semi-autonomous cars assist drivers in avoiding distractions and veering off the road by providing real-time analytics and decisions on driving patterns. Fog computing can also reduce the transfer of gigantic volumes of audio and video recordings generated by police dashboard and video cameras. Cameras equipped with edge computing capabilities could analyze video feeds in real time and only send relevant data to the cloud as needed.
Fog computing consists of a control and a data plane. On the data plane, fog computing enables computing services to reside at the edge of the network as opposed to servers in the datacenter.
Compared to cloud computing, fog computing emphasizes:
1. proximity to end users
2. dense geographical distribution
3. local resource pooling
4. latency reduction for QoS
5. edge analytics/stream minig
Fog computing will gain prominence as IoT usage grows. With inexpensive low power processing and storage becoming available we can expect computation to move even more closer to the edge and become ingrained in the same devices that are generating data, creating even greater possibilities for inter-device intelligence and interactions.