Moving enterprise applications to a centrally located cloud gained wide acceptance over the last decade, due to significant operational and cost efficiencies over on-prem cloud. However, as individual and enterprise customers became accustomed to applications that deliver a real-time user experience, the demand for network solutions such as edge computing, to enable faster data processing grew significantly in recent years. This posed a new challenge to enterprises and telecommunication companies in terms of reducing the network latency, to deliver an optimal application response-time and user experience.
The new opportunity for the Telecommunications industry – Edge Computing
For telecommunications companies, as average revenue per user (ARPU) from data consumption alone stagnated, modernizing their networks to collaborate with enterprise clients, unlocked new revenue opportunities. Delivering applications and services by leveraging high-speed data processing capabilities, has enabled telecommunications to become a key enabler for organizations in optimizing internal processes and offer better customer experience.
So, how do telecommunications companies leverage edge computing technology to reduce latency and ensure a highly efficient network? Edge computing captures, stores, analyzes, and processes data closer to the location where data is originally generated. This helps telecommunications offer a network solution with higher efficiency, availability, reliability, and scalability. Telecommunications companies now find themselves in a prime position for new revenue opportunities with edge computing, delivering a real-time experience for their customers across many industries, such as the following two examples.
- Delivering Edge Computing to Smart Factories
Large manufacturing plants are typically situated in remote locations for better cost efficiencies and often underserved by WANs (wide access networks). Until a few years ago, applications that automated manufacturing processes would communicate with the core network of a Communication Service Provider (CSP), which would then work with a back-end server. These back-end servers would have the analytical capacity to send a calibrated response back to the application using the same route. The response time would depend on 1) the geographical location of servers, 2) the quantity of data generated, and 3) the back-end servers’ capacity to manage multiple incoming requests.
However, with an exponential rise in automation, the amount of data that’s collected from sensors and IoT devices has dramatically increased. A smart factory can generate millions of gigabytes of data every day. Bandwidth limitations and network disruptions can potentially lead to untenable latencies when processing received data from these sensors and IoT devices and, thus, negatively impact automated processes. For a factory to function efficiently and generate ideal outputs, data must be processed with minimal latency. This is where edge computing comes in.
Edge servers are located closer to data sources inside the manufacturing plants to reduce latency and data volumes throughout the network, thus enhancing the overall network efficiency. Edge computing also safeguards the application from network outage scenarios where communications between the application and the back-end servers are interrupted. Lastly, it reduces the load for back-end servers so the enterprise can repurpose an application for other tasks. For example, organizations typically prefer machine learning models that are centrally located on the cloud server. Those machine learning models help enhance the accuracy of applications located on the edge server over time.
2. Multi-access Edge Computing (MEC) Powering Autonomous Vehicles
In regular edge computing, data is generated at a location that’s fixed, like a retail store, to enhance the shopping experience. Now, let’s imagine a scenario where similar computing power is critical, but across dynamic locations, like self-driving cars, for example. An autonomous driving car can generate data up to 1GB per second. Since response time is extremely critical in case of autonomous vehicles, mobility edge computers are located at the base station of a radio access network or installed at the edge compute data center for the network itself. This architecture enables MEC to reduce latency of response to as low as ten milliseconds.
What does this mean for Telecommunications?
Edge computing has a wide range of applications across video analytics, security, location services, IoT, smart wearables, augmented reality and more. With roll outs of technologies, such as 5G and Wi-Fi 6, edge deployments are bound to boost data virtualization and automation. It has been estimated that the overall global edge computing market will reach USD 43.4 billion by 2027, growing at more than 35% annually until 2027. The telecommunications industry can seize this growth by building the right network ecosystem, with the help of technology partners and providing their enterprise clients with differentiated services for their customers.
For over 3 decades, Orion has helped clients across the Telecommunications industry with OSS/BSS modernization, network virtualization, cloudification, engineering services, product development and more.
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