Edge Computing is an approach to cloud computing that brings data processing and storage closer to the sources of this information. It’s a “smart” way of leveraging the low cost, availability, scalability, and resiliency benefits cloud infrastructure provides while maintaining control over sensitive data.
Edge Computing is a great solution for IoT, Real-time Analytics, and Machine Learning. Edge computing allows data to be processed at the originating source without sending it over the internet. This reduces latency and ensures that “smart” systems can rely on critical information as quickly as possible.
The Growing Need for Edge Computing
Edge computing for future ready networks is the next step in cloud evolution. Cloud applications are becoming more demanding, requiring higher bandwidth and lower latency to deliver a better experience across multiple devices. These demands require infrastructure that’s distributed at the network edge with intelligence built into it for low-latency processing of data closer to where they’re being generated or used.The benefits of edge computing include:
- Faster data processing and faster application response times. Applications perform better, thanks to reduced latency and higher bandwidth between users and the cloud at large. This allows for a lower cost per transaction or interaction with the end-user because requests can be processed in real-time closer to where they’re generated without sending traffic back to the cloud.
- Better security and privacy protection. The risk of a data breach is greatly reduced because sensitive information doesn’t have to be sent back and forth across potentially vulnerable networks while being processed in the cloud, where it’s exposed for a longer period of time. Instead, data can stay safe on edge devices until they’re further processed in the cloud.
- Improved infrastructure flexibility and scalability. As data traffic increases, edge devices can be easily scaled up or down in terms of processing power without affecting application performance because they’re not overwhelmed with heavy workloads like they would if you were to add more load balancers, servers, databases, or other compute resources at a central location.
What Are Some Use Cases For Developing a Large-Scale Techno-centric Ecosystem?
To harness the potentials of edge computing in developing next-gen digital networks, STL is building powerful and customized uses cases that include:
Surveillance Solution for Safe Cities
Safe City is an important program of the Indian Government to build secure urban living spaces and ensure citizen’s safety through 360-degree situational awareness powered by advanced video analytics and surveillance platforms. Some solutions of this program are designing using Edge Computing, including:
- Intelligent Video Analytics: The surveillance data will be processed at the edge at real-time actions and insights. Advanced video analytics can retrieve insights like unattended object tracking, camera tampering, motion detection, object tracking, etc.
- Automatic Number Plate Recognition: The live video footage that is streamed from multiple cameras is locally processed to locate the number plate of the vehicle to share only relevant data for better analysis on traffic violations.
Content Delivery Network
With the exponential increase in virtual classrooms, online gaming, and work from home, the prominence of content delivery with a snag-free user experience has become a central need. Additionally, there is increasing pressure on gaming and content providers to offer fast streaming of high-quality content. Edge computing is being used to facilitate OTT and virtual applications. Caching units are positioned at the network’s edge to provide the streaming of video or training content to improve content delivery networks’ abilities.
Data Centre On Wheels
In response to remote war or disasters, prompt decisions must be taken with regard to the investigation, resource deployment, and mobilization. It is powered by a movable command and control center, which analyzes the intelligence through a movable set-up of the data center. DC, CCC, or other innovative solutions are needed for such operations.
E2E Edge Computing Infrastructure for Telecom Service Providers
To cater to the expectations of QoS, latency, and speed requirements for various applications, edge computing will be more important in 5G access networks. For some use cases, the end-user experience can be improved by the network operator through context-aware services. Moreover, STL allows end-to-end edge computing infrastructure not merely from a software-centric perspective but also by developing customized racks solutions together with integrated power and cooling.