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What is Edge Computing? Get to know everything about it here!
Edge Computing
Edge computing is a digital computing model, which is used to bring
calculations and data storage near data sources. Edge computing is used to shot
down response time and save bandwidth. Edge computing can be used in
virtualization, to make it easy to run multiple applications on edge servers.
Karim
Arabi, the vice president and Engineer at Qualcomm (He is responsible for
research and development of ASIC and new product development) in an IEEE DAC
(Institute of Electrical and Electronics, Design Automation Conference) 2014
Keynote and MTL’s Seminar 2015 mentioned that Cloud computing works on big data
while Edge computing works on “instant data”, the data made by the user or sensor
instantaneous. A similar term to edge computing is fog computing.
Smart
devices usually use Edge computing, where sensors and other instruments are
collecting and processing data.
What is the
Main Purpose Behind it?
The main purpose is to deliver compute, storage, and bandwidth to bring the
data input users closer. Edge computing was first used by virtualizing network
services over WAN networks far from a data centre. Cloud edge computing is
combining virtualization and cloud computing to create a huge quantity of
distributed nodes that can be applied to divert used cases. Open-source edge
computing and many proprietary capabilities are there who are not using or
relying on the distributed cloud. But applications need the versatility of cloud edge.
With the growing need for cloud edge, it won’t be wrong to say it will be a high
demand.
More about Mobile Edge Computing
Mobile edge computing is also known as Multi-access Edge Computing, which is used to extend cloud computing capabilities by bringing it to the edge of the network. Mobile Edge computing is used to advance existing applications, like content delivery and caching. Mobile edge computing also helps processes to take place in base stations, central offices and on other points of the network.
Cloud gaming
and multiplayer gaming would improve under Mobile edge computing, not only
games but other applications like Enterprise Mixed reality (MR), Augmented
reality (AR), Virtual Reality (VR), Video analytics, Real-time detection and
connected autonomous.
What exactly is Edge Network?
Edge
network refers to an area where a device or local network interfaces with the internet. The edge network is close to the devices that it is communicating with
and also to the entry point of the network. Due to the increasing threat
towards cyber security, the devices connected to the interconnection of
networks is growing cybercriminals, who are using this as an opportunity to
exploit vulnerabilities in networks, applications and unsecured devices.
With
everything we have seen by now we know that edge computing technology is a very
important part of cloud security and usage. Edge computing technology is
crucial in the management of failovers, to keep it alive we need Edge
computing technology. Moreover, edge computing technology must provide actions
to recover from a failure and alerting the user about the incident.
Privacy and Security
Now
let’s talk about privacy and security the distributed nature used in the model
of edge computing makes a shift in security schemes used in cloud computing.
Edge computing allows data to travel between different distributed nodes
connected through the internet, and because of that, it requires special
encryption mechanisms that are not dependent on the cloud. Using edge nodes helps in
limiting the choice in terms of security methods. It is always a possibility to increase
privacy by minimizing the transmission of sensitive information to the cloud.
Scalability
in distributed network faces different issues, like it must take in the heterogeneity of devices, having different performance and energy constraints,
the highly dynamic condition, and also the reliability of the connections
compared to the more robust infrastructure of cloud data centres. Sometimes there
are security requirements that may introduce further latency in the communication
between nodes, which may slow down the scaling process.
Even
with great performance, speed is very important. In Edge computing, it brings
analytical computational resources closer to the end-users to increase the responsiveness and throughput of applications. A well-designed
edge platform would significantly outperform an old house cloud-based system.
Few applications rely on short response times. So, speed is crucial in edge
computing. It also makes edge computing more feasible than the cloud. Some examples
of the same are autonomous driving, health/ human/ public safety-relevant,
human perception as face recognition.
How efficient is this?
Using the closeness of analytical resources to end-users, analytical tools and artificial intelligence tools can run on the edge of the system. With this placement at the edge, it helps enlarge results in efficiency and brings many advantages with it. The usage of edge computing technology between the client device and the internet results in efficiency savings is promising. Also, it avoids transmission over the internet results in significant bandwidth savings, which furthermore grows the efficiency.
Voice recognition is also an example of
how good edge computing efficiency is. For a perfect recognition performed
locally, it can be possible to send it directly to the cloud rather than audio
recordings, furthermore, that reduces the bandwidth.
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