Edge computing enables data to be analyzed, processed, and transferred at the edge of a network. Meaning, the data is analyzed locally, closer to where it is stored, in real-time without latency, rather than send it far away to a centralized data center.
The increase of IoT devices at the edge of the network is producing a massive amount of data to be computed at data centers, pushing network bandwidth requirements to the limit. Despite the improvements in network technology, data centers cannot guarantee acceptable transfer rates and response times, which could be a critical requirement for many applications. Furthermore, devices at the edge constantly consume data coming from the cloud, forcing companies to build content delivery networks to decentralize data and service provisioning, leveraging physical proximity to the end-user.
Similarly, Edge Computing aims to move the computation away from data centers towards the edge of the network, exploiting smart objects, mobile phones, or network gateways to perform tasks and provide services on behalf of the cloud By moving services to the edge, it is possible to provide content caching, service delivery, storage, and IoT management resulting in better response times and transfer rates. At the same time, distributing the logic in different network nodes introduces new issues and challenges.
Cloud computing Vs Edge Computing
a). Cloud computing is used to process data that is not time-driven, while edge computing is used to process time-sensitive data. Cloud computing takes a minute to days response time and later takes milliseconds.
b). Edge is a distributed cloud, with cloud functionality placed nearer to the device or user or the source of data.
Advantages Of Edge Computing
1). Improved Performance:
Besides collecting data for transmission to the cloud, edge computing also processes, analyses, and performs necessary actions on the collected data locally. Since these processes are completed in milliseconds, it’s become essential in optimizing technical data, no matter what the operations may be.
Transferring large quantities of data in real-time in a cost-effective way can be a challenge, primarily when conducted from remote industrial sites. This problem is remedied by adding intelligence to devices present at the edge of the network. Edge computing brings analytics capabilities closer to the machine, which cuts out the middle-man. This setup provides for less expensive options for optimizing asset performance.
2). Reducing Operational Costs:
In the cloud computing model, connectivity, data migration, bandwidth, and latency features are pretty expensive. These barriers are removed by edge computing, which has a significantly less bandwidth requirement and less latency
3). Quick Response Time:
Edge computing allows for quicker data processing and content delivery while streaming a video on platforms like Netflix or accessing a library of video games in the cloud.
4). Future Technology Enabled:
Technologies such as 5G wireless technology and artificial intelligence enable faster response times, lower latency (delay), and simplified maintenance in computing.
5). Localized solution:
It is preferred over cloud computing in remote locations, where there is limited or no connectivity to a centralized location. These locations require local storage, similar to a mini data center, with edge computing providing the perfect solution for it.
That data doesn’t need to be sent over a network as soon as it processed; only important data is sent. Therefore, an edge computing network reduces the amount of data that travels over the network.
Examples Of Edge Computing in use today:
a). Oil & Gas monitoring
b). Traffic Management
c). Autonomous Vehicles
d). Healthcare devices
e). Video Conferencing
f). Smart Speakers
g). Security Solutions
Future Of Aspect
Experts believe the true potential of edge computing will become apparent when 5G networks go mainstream in a year from now. Users will be able to enjoy consistent connectivity without even realizing it.
Sources: Indian Express