Access to centralized cloud servers had led telecom network operators and their enterprise customers to assume that hyperscale public cloud service offerings would potentially enhance the storing and processing of all data being generated.
However, performance issues arising from network latency translate into public cloud services being unsuitable for some data-intensive computing activities, such as analyzing complex data workloads and delivering real-time insights.
Cloud edge computing, a form of distributed computing, where the device that collected the data, or an on-premises gateway, or an off-premises edge node close by, solves many problems associated with wide-area network latency.
Cloud Edge Computing Market Development
According to the latest worldwide market study by Juniper Research, the telecom network operator spend on Multi-access Edge Computing (MEC) will grow from $2.7 billion in 2020 to reach $8.3 billion in 2025.
Multi-access edge computing (formerly mobile edge computing) is a network architecture that enables cloud computing capabilities and an IT service environment at the edge of the cellular network -- also, potentially any network.
In particular, mobile service providers will invest heavily in upgrading their communication network capacities and IT systems infrastructure to support the increasing data generated by emerging fifth-generation (5G) network apps.
The market study also revealed that by 2025, the number of deployed MEC nodes will reach 2 million globally by 2025 -- that's up from just 230,000 in 2020.
The market study also revealed that by 2025, the number of deployed MEC nodes will reach 2 million globally by 2025 -- that's up from just 230,000 in 2020.
These devices, which take the form of access points, base stations, and routers, will play a vital role in managing the vast quantities of Internet of Things (IoT) data generated by connected vehicles, smart city systems, and other emerging data-intensive services.
The new research findings uncovered that this increase in investment is a result of mobile service providers enhancing key network functions, by moving systems used for processing data from core network locations, to base stations at the edge of their networks.
The new research findings uncovered that this increase in investment is a result of mobile service providers enhancing key network functions, by moving systems used for processing data from core network locations, to base stations at the edge of their networks.
Juniper analysts anticipate that the capabilities of 5G technologies -- such as high throughput, low latencies, and high device densities -- will necessitate the deployment of MEC nodes in urban areas.
The research also identified 'smart cities' as a key emerging market sector that will benefit from MEC node roll-outs, as operators and planning authorities identify how best to install 5G-compatible edge nodes.
The research also identified 'smart cities' as a key emerging market sector that will benefit from MEC node roll-outs, as operators and planning authorities identify how best to install 5G-compatible edge nodes.
These latest findings suggest that the involved parties explore utilizing existing city structures, such as street lighting and sidewalks, to mitigate issues of space limitation inherent to densely-populated areas.
Outlook for Cloud Edge Computing Applications Growth
The research forecasts over 920 million individuals will benefit from edge-enhanced Internet connectivity by 2025; rising from 100 million individuals in 2020.
Outlook for Cloud Edge Computing Applications Growth
The research forecasts over 920 million individuals will benefit from edge-enhanced Internet connectivity by 2025; rising from 100 million individuals in 2020.
Services, such as music streaming, digital TV services, and cloud gaming, will be the biggest beneficiaries of the ultra-low latency provided by network operators’ increasing roll-outs of MEC nodes over the next 5 years.
That said, I'm eager to learn more about how edge computing and 5G networks help to solve enterprise big data analytics challenges, in practice. While I don't doubt that these solutions have merit, I need to see the results from a study of common use cases that have proven to be the most challenging.