In today’s rapidly evolving digital landscape, Communication Service Providers (CSPs) are persistently seeking ways to enhance their network performance, broaden their service offerings, and meet the growing demands for low-latency and high-bandwidth applications.
An emergent strategy is integrating existing infrastructure with edge cloud technologies. This enables CSPs to apply their network infrastructure and expertise to emerging categories of real-time data processing and analytics.
A recent survey by Inform found that CSP investment in cloud infrastructure had increased by 25 to 50%, with edge cloud leading the charge. Participants highlighted that, as workloads are moved to the edge, the demand for edge cloud services will undoubtedly increase.
Here’s how CSPs can combine their existing infrastructure with edge cloud services.
Defining the relationship between edge cloud and CSPs
At the crux, edge cloud involves decentralising computational tasks and data storage, moving them closer to the locations where they are needed.
This drastically reduces latency, allowing real-time processing and decision-making. For CSPs, deploying edge cloud technologies is about placing network services, applications, and data storage at the network edge – closer to the devices and users that require them.
Edge cloud essentially acts as a middle ground between centralised, large-scale cloud networks and smaller locally-distributed networks.
So, instead of edge devices sending and receiving data to a singular cloud via long connections, they send and receive data to clouds distributed at the edge, which cuts latency. Edge clouds multiply the uses of “instant” real-time edge data with cloud storage and orchestration. You can visualise this as a “micro data centre” deployed at the edge.
This is where CSPs come in – as they own and manage the network infrastructure required to connect edge devices and edge clouds. CSPs can combine their network infrastructure solutions, such as public and standalone 5G, with network slicing and other localised services.
This enables them to deploy and optimise low-latency edge clouds and related services for different businesses and organisations.
Why instant data matters
Combining edge devices with edge cloud for localised data processing and decision-making is vital for modern applications that demand ultra-low-latencies. Some examples include:
- Autonomous vehicles (AVs) and traffic or logistical applications: Autonomous vehicles require near-instantaneous decision-making. They feature thousands of sensors; some process data locally, whereas others must transmit that data elsewhere for processing. Sending data to a large-scale public cloud is slow and cumbersome. Instead, the vehicle could send data to a local edge cloud explicitly designed for ultra-low-latency processing. CSPs are deploying edge clouds for AVs in urban areas across the world.
- Healthcare: Edge computing has expanded access to services like telehealth-based therapy with Augmented Reality (AR) and Virtual Reality (VR) technologies. Healthcare providers can deliver remote care directly to patients in their homes or at healthcare facilities, increasing accessibility and enabling real-time decision-making at the edge
- Manufacturing: Edge computing has sped up industrial operations by enabling reliable preventive maintenance and on-premises data analytics. Moreover, manufacturing plants are often isolated, meaning they benefit from localised network hardware that CSPs can deliver. This also optimises bandwidth usage, allowing for video streaming directly at the edge.
- Entertainment: Edge computing has enhanced the experience at live venues such as concerts and sports events. Edge clouds enable immersive AR and VR experiences, intelligent parking solutions, ticketless entry, and contactless payment systems for food and souvenirs.
Transitioning to the edge
Deploying network infrastructure and services to the edge is complex, involving a constellation as technologies and services.
Decentralising network functions
CSPs are shifting computational tasks away from centralised data centres to edge nodes, which are physically closer to the users. This method accelerates data delivery, enabling near-real-time data processing.
A key technology here is Network Function Virtualization (NFV), which replaces traditional, hardware-dependent network functions with flexible, software-based solutions. Here, CSPs can reduce hardware expenditure and reduce the time to deploy new network services.
Adopting multi-access edge computing (MEC)
MEC provides a cloud-based environment at the network’s edge, allowing high bandwidth, low latency, and real-time access.
Implementing MEC allows CSPs to host applications and processes closer to the users, reducing latency and increasing performance. This is foundational to delivering edge data analytics.
MEC is especially beneficial for applications that demand real-time processing, such as AR, autonomous vehicles, and Internet of Things (IoT) devices.
Enhancing security
With the proliferation of data at the edge of the network, local and on-premises data security becomes paramount.
CSPs can fortify their network edge by deploying advanced edge firewalls, intrusion detection systems, and robust data encryption methods.
Case study: AT&T and edge computing
In 2021, AT&T announced integration with their edge 5G and fibre networks and Google Cloud. They’re planning on rolling out edge clouds to 15 US cities.
Verizon is also planning to create 20 MEC sites across the US. In collaboration with Google Cloud, AT&T delivers a low-latency, high-speed network edge computing environment for its customers, combining Google Cloud services with their distributed 5G and fibre networks.
AT&T deploys AT&T Multi-Access Edge Compute (MEC) to move network capabilities, applications, and services closer to the user, reducing latency and enabling real-time insights.
This setup enables customers to deploy critical real-time workloads as close to their application as possible for the purposes of real-time analytics and decision-making. Meanwhile, customers have more data storage options, including on-premises, in their data centre, or in the cloud.
AT&T has also made notable strides in deploying Network Edge Compute (NEC) technology. NEC combines AT&T’s network capabilities with Google Cloud’s cloud services right at the network’s edge.
This allows businesses to tap into compute power as if it were part of their local network infrastructure, enabling applications and workloads requiring low latency, like autonomous vehicles, drones, and AI-driven applications.
The path forward
The journey of CSPs towards edge cloud integration is undoubtedly complex, requiring strategic planning and significant virtualisation.
However, deploying edge cloud services is sensical as real-time data demands surge. Edge network infrastructure supports numerous industries and sectors, from autonomous vehicles to healthcare, manufacturing and cloud gaming.
CSPs embracing edge clouds will unlock new opportunities, though intricate, this paves the way for a paradigm shift in network services and deployment of low latency workloads at the edge.
