Fog Computing
Fog Computing is a paradigm that extends Cloud computing and services to the edge
of the network. Similar to Cloud, Fog provides data, compute, storage,
and application services to end-users. The distinguishing Fog characteristics are its
proximity to end-users, its dense geographical distribution, and its support for
mobility. Services are hosted at the network edge or even end devices such as settop-boxes or access points. By doing so, Fog reduces service latency, and improves
QoS, resulting in superior user-experience. Fog Computing supports emerging
Internet of Everything (IoE) applications that demand real-time/predictable latency
(industrial automation, transportation, networks of sensors and actuators). Thanks to
its wide geographical distribution the Fog paradigm is well positioned for real time big
data and real time analytics. Fog supports densely distributed data collection points,
hence adding a fourth axis to the often mentioned Big Data dimensions (volume,
variety, and velocity).
Unlike traditional data centers, Fog devices are geographically distributed over
heterogeneous platforms, spanning multiple management domains. Cisco is
interested in innovative proposals that facilitate service mobility across platforms, and
technologies that preserve end-user and contentsecurity and privacy across
domains.
Fog provides unique advantages for services across several verticals such as IT,
entertainment, advertising, personal computing etc. Cisco is specially interested in
proposals that focus on Fog Computing scenarios related to Internet of Everything
(IoE), Sensor Networks, Data Analyticsand other data intensive services to
demonstrate the advantages of such a new paradigm, to evaluate the trade-offs in
both experimental and production deployments and to address
potential research problems for those deployments.
Full Description:
While Fog conceptually extends Cloud computing and leverages Cloud's underlying
technologies, Fog, by definition, spans wider geographic locations than Cloud, and in
a denser way. Also, Fog devices are much more heterogeneous in nature, ranging
from end-user devices, access points, to edge routers and switches. To
accommodate this heterogeneity, Fog services are abstracted inside a container for
ease of orchestration. Example container technologies are Java Virtual Machine
(JVM), and Linux containers. Proposals that investigate service mobility across Fog
platforms are of particular interest. Specifically,
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Technologies that support workload mobility between Cloud and Fog platforms
based on policies and the capability of the underlying infrastructure.
Technologies that optimize different aspects of service mobility.
Fog services will be orchestrated across management domains; services will be
provisioned, monitored and tracked across these domains. Proposals investigating
security and privacy in the context of Fog Computing are encouraged. Specifically,
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Privacy, security threat analysis for various Fog players (ex: service provider, enduser, content provider) in the context of different Fog service verticals (ex: IoE,
Sensor Networks, Data Analytics, IT, entertainment, personal computing).
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Technologies that preserve security and privacy of user/content across domains.
Technologies that seamlessly integrate and extend existing Cloud security/privacy
solutions in the context of Fog.
While Fog provides unique advantages for services across several verticals such as
IT, entertainment, advertising, personal computing etc., Cisco is specially interested
in investigating Fog advantages for Big Data services in several verticals including
IoE. Specifically, innovations in compute, storage offerings for data intensive services
such as the following:
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Interplay between the Fog and the Cloud. Typically, the Fog platform supports realtime, actionable analytics, processes, and filters the data, and pushes to the Cloud
data that is global in geographical scope and time.
Data collection and analytics (pulled from access devices, pushed to Cloud)
Data storage for redistribution (pushed from Cloud, pulled by downstream devices)
Technologies that facilitate data fusion in the above contexts.
Analytics relevant for local communities across various verticals (ex:
advertisements, video analytics, health care, performance monitoring, sensing etc.)
Methodologies, Models and Algorithms to optimize the cost and performance
through workload mobility between Fog and Cloud.
Computer networks can be classified into different types based on their scale of
operation. They include:
LAN: Local Area Networks cover a small physical area, like a home, office, or a small
group of buildings, such as a school or airport.
WLAN: Wireless Local Area Networks enable users to move around within a larger
coverage area, but still be wirelessly connected to the network.
WAN: Wide Area Networks cover a broad area, like communication links that cross
metropolitan, regional, or national boundaries. The Internet is the best example of a
WAN.
MAN: Metropolitan Area Networks are very large networks that cover an entire city.
SAN: Storage Area Networks help attach remote computer storage devices, such as
disk arrays, tape libraries, and optical jukeboxes, to servers in such a manner that
that they appear to be locally attached to the operating system.
CAN: Controller Area Networks allow micro controllers and devices to communicate
with each other without a host computer.
PAN: Personal Area Networks are used for communication among various devices,
such as telephones, personal digital assistants, fax machines, and printers, that are
located close to a single user.
GAN: Global Area Networks support mobile communications across an arbitrary
number of wireless LANs and satellite coverage areas.
INTERNETWORK: Internetworking is the process of connecting two or more distinct
computer networks or network segments through a common routing technology.