Edge Compute Infrastructure – Key enabler techno-centric ecosystems

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The need for time-driven data processing is powering the edge compute ecosystem

Now days, the acceleration of newer use cases and applications has increased the ante on customer experience. Service Providers across the world are looking to gain a competitive advantage by delivering high speed, bandwidth, and low latency.

With an expected ~300 Bn applications, 29 Bn devices, 5 Bn connected humans and with ~50% workloads running closer to the users by 2021, time-driven data processing will be absolutely critical.

While traditionally, data centres have been handling huge volumes of data processing which was less time critical and required lower bandwidth, these above mentioned trends are spurring an evolution in the data centre ecosystem. Newer solution architectures are leveraging the power of edge computing, to process time sensitive data to enable a myriad of experiences for the end users. Here are a few key parameters that edge computing will achieve for newer technologies and emerging business models:

  1. Enabling near zero latency for 5G
  2. Reducing operational costs by lowering bandwidth demands till cloud DC
  3. Increase data security and privacy of sensitive data
  4. Enable business operation agility, efficiency and reliability

Depending upon the need of the applications and end user demands, edge compute can be deployed at the edge of the network (For example – vOLTHA [Virtualised OLT hardware Abstraction], v-Controller, v-BBU Base Band Unit(DU+CU) or on the end node/devices (For example – Digital kiosks or access points).

Figure 1: Reference Architecture – Edge Compute

Edge Computing

Some powerful use cases for building of large scale techno-centric ecosystems

To leverage the power of edge computing in building next-gen digital networks, STL is designing highly customised and powerful use cases which will create transformational impact for service providers, citizens and governments. Some of the use-cases are:

  1. Surveillance solutions for Safe Cities

Safe City is a key program of the Government of India to create secure urban living spaces, and to ensure safety of the citizens through 360o situational awareness enabled by surveillance platforms and advanced video analytics.

Few solutions for the Safe City Program which are designed with Edge Compute are:

  • Facial Recognition System (FRS) – Local processing powerhouse at the edge captures the facial images of the humans, crops and analyses it to support crime detection and handling using GPU infrastructure at the edge for running sophisticated ML algorithms[VS1] 
  • Automatic Number Plate Recognition (ANPR) – In this system, live video footages that are streamed from different cameras are processed locally to extract the number plate information of the vehicle to transmit only relevant data to a central server for more insightful analysis on traffic violations
  • Intelligent Video Analytics (IVA) – All the surveillance data is processed at the edge for real-time insights and actions. Advanced video analytics can extract insights such as motion detection, camera tampering, unattended object tracing, object tracking and many such features.

In surveillance scenario, since analytics can be performed at the edge layer, near about 60-70% of bandwidth costs can be saved.

  2. Internet of Things (IoT) for Smart Cities

Smart City is an ambitious program to develop city infrastructure which is technology driven, using sensors, automation, central monitoring and control. Some of the smart city use cases which are designed with edge compute by leveraging bandwidth and instant processing are:

  • Automatic Traffic Control System – Control and management of the traffic lights can be processed at local processing units placed at the edge of the network
  • Red Light Violation Detection System – Violation of Red light at traffic junctions can be caught with the help of local processing units and challans can be generated instantaneously
  • Automatic Vehicle Location System– AVL helps in determining the geographic location of vehicles and in transmitting this information to a central source for enabling use cases like public transport management, tracking and mobilising ambulances and tackling emergency situations like accidents.

Above solutions can be implemented for any kind of transportation, in the city, in mining sites or in townships and can make governance and civic operations automated and effective.

  3. Content Delivery Network (CDN)

With the unprecedented rise in virtual classrooms, work from home and online gaming, the importance of content delivery along with snag-free user experience has become the need of the hour. Also, there is growing pressure on content and gaming providers to deliver fast streaming of high-quality content to end-users, and to meet both localised and distributed peaks in demand. Edge computing is increasingly been used to cater to the virtual and OTT applications. Caching units are placed at the edge of the network to deliver the training contents or streaming of video and localised processing units are used to enhance the capabilities of content delivery networks.

  4. Data Centre / Command and Control Centres on Wheel for Defence, Homeland Security & Disaster Management

In the wake of disasters or remote war like operations undertaken by defence and paramilitary forces, crucial and instant decisions are needed to be taken in terms of reconnaissance, resource deployment and action mobilisation. This is enabled by a movable command and control centre which accesses intelligence through a movable data centre set-up. Innovative solutions like the CCC or DC on Wheels are required for these kind of operations. While enabling flexible backhaul from wireline or wireless and front hauling through a drone as a network extender, this solution is a perfect case example of edge computing.

In a drone operation, target latency between the drone and its controlling applications is less than 10ms. To operate it with minute perfection, edge compute does the operation efficiently by quickly analysing the data and makes a decision faster with adequate intelligence and low latency communications.

  5. E2E Edge Compute Infrastructure for telecom service providers

To satisfy the QoS, speed and latency requirements for many applications such as AR/VR and Video Gaming, Edge Compute will be much more prevalent in 5G access networks. For some of the associated use cases of vRAN as depicted in Figure.1 above, the quality of experience for the end user can be enhanced by the network operator by offering context-aware services. STL enables end to end the edge computing infrastructure not only from a software-defined perspective, but also by designing customised racks solution (5G Edge Mantra) with inbuilt power and cooling for addressing different customer requirements.

Start your journey to the Edge, Now!

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