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.
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