Network edge capacity forecast: The role of hyperscalers

Developers need to see sufficient edge capacity

Edge computing comprises a spectrum of potential locations and technologies designed to bring processing power closer to the end-device and source of data, outside of a central data centre or cloud. This report focuses on forecasting capacity at the network edge – i.e. edge computing at edge data centres owned (and usually operated) by telecoms operators. 

This forecast models capacity at these sites for non-RAN workloads. In other words, processing for enterprise or consumer applications and the distributed core network functions required to support them. We cover forecasts on RAN as part of our Telco Cloud research services portfolio.

Forecast scope in terms of edge locations and workload types

Source: STL Partners

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The output of the forecast focuses on capacity: number of edge data centres and servers

STL Partners has always argued that for network edge to take off, developers and enterprises need to see sufficient edge capacity to transform their applications to leverage its benefits at scale. The forecast seeks to provide an indication for how this will grow over the next five years, by predicting the number of edge data centres owned by telecoms operators and how many servers they plan to fill these up with.

Hardware vendors have been evolving their server portfolios for a number of years to fit the needs of the telecoms industry. This started with core network virtualisation, as the industry moved away from an appliance-based model to using common-off-the-shelf hardware to support the virtualised LTE core.

As infrastructure moves “deeper” into the edge, the requirements for servers will change. Servers at RAN base stations will not have full data centre structures, but need to be self-contained and ruggedised. 

However, at this stage of the market’s maturity, most servers at the network edge will be in data centre-like facilities. 

There are three key factors determining a telco’s approach and timing for its edge computing data centres

Telecoms operators want to build their network edge capacity where there is demand. In general, the approach has been to create a deployment strategy for network edge data centres that guarantees a level of (low) latency for a certain level of population coverage. In interviews with operators, this has often ranged from 90-99% of the population experiencing sub-10 to 20 millisecond roundtrip latency for applications hosted at their network edge.

The resultant distribution of edge capacity will therefore be impacted by the spread of the population, the size of the country and the telecoms operator’s network topology. For example, in well connected, small countries, such as the Netherlands, low latencies are already achievable with the current networks and location of centralised data centres.

Key factors determining network edge build​

Source: STL Partners

The actual number of sites and speed at which a telecoms operator deploys these sites is driven by three main factors: 

Factor 1: edge computing strategy;

Factor 2: the speed at which it has or will deploy 5G (if it is a mobile operator);

Factor 3: the country’s geographic profile.

Details on the evidence for the individual factors can be found in the inaugural report, Forecasting capacity of network edge computing.

Table of contents

  • Executive summary
  • Introduction to the forecast
  • Key findings this year
  • Regional deep-dives
  • Role of hyperscalers
  • Conclusions
  • Appendix: Methodology

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Forecasting capacity of network edge computing

Telco edge build has been slower than expected

Telecoms operators have been planning the deployment of edge computing sites for at least the last three years.

Initially, the premise of (mobile) edge computing was to take advantage of the prime real estate telecoms operators had. Mobile operators, in particular, had undergone a process of evolving their network facilities from sites which housed purpose-built networking equipment to data centres as they adopted virtualisation. The consolidation of networking equipment meant there would be spare capacity in these data centres that could easily host applications for enterprises and developers.

That evolution has now been accelerated by the advent of 5G, a mobile generation built on a software-based architecture and IT principles. The result will be a proliferation of edge data centres that will be used for radio access network and core network hardware and software.

However, the reality is that it has taken time for telcos to deploy these sites. There are multiple reasons for this:

  1. Cost: There is a cost to renovate an existing telco site and ensure it meets requirements common for world-class data centres.
  2. Demand: Telcos are hesitant to take on the risk of building out the infrastructure until they are certain of the demand for these data centres.
  3. 5G roll-out: Mobile operators have been prioritising their 5G RAN roll-out in the last two years, over the investment in edge data centres.
  4. Partnership decisions: The discussion around who to partner with to build the edge data centres has become more complicated, because of the number of partners vying for the role and the entrance of new partners (e.g. hyperscalers) which has slowed down decision-making

Download the additional file to view the accompanying spreadsheet

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Early adopters have taken significant strides in their edge strategy in 2021

2020 and 2021 have been seen as inflection points as a number of leading telecoms operators have launched edge sites: e.g. AT&T, Verizon, Cox Communications, SK Telecom and Vodafone. Arguably, this was triggered by AWS announcing partnerships on AWS Wavelength with four telecoms operators in November 2019, with more recently announced (e.g. Telstra in 2021).

Going forward, key questions remain on the trajectory of telco edge build:

  • How many edge data centres will telcos build and make available for consumer/enterprise applications?
  • How much capacity of telco edge computing will there be globally?
  • How much of telco edge computing will be used for distributed core network functions vs. consumer/enterprise applications?
  • What proportion of telco edge data centre capacity will be taken up by hyperscalers’ platforms?

This report seeks to forecast the capacity at telecoms operators’ edge data centres until 2025 and provide clarity on the nature and location of these sites. In other words, how many sites and servers will be available for running applications and where will these sites be located, both physically and logically in the telecoms operators’ networks.

Before reading this report, we would recommend reading STL Partners’ previous publications on telco edge computing to provide context for some of the key themes addressed, for example:

The report focuses on network edge computing sites

Edge computing comprises of a spectrum of potential location and technologies designed to bring processing power closer to the end-device and source of data, outside of a central data centre or cloud. This report focuses on forecasting capacity at the network edge – i.e. edge computing at edge data centres owned (and usually operated) by telecoms operators.

The initial version of the forecast models capacity at these sites for non-RAN workloads. In other words, processing for enterprise or consumer applications and the distributed core network functions required to support them. Future versions of the forecast will expand to RAN.

Forecast scope in terms of edge locations and workload types

The report covers two out of three scenarios for building the network edge

Table of content

  • Executive summary
  • Introduction
  • There are 3 key factors determining telco edge data centre build out
  • Logically, most network edge will be in the transport aggregation layer
  • Geographically, we will see a shift in the concentration of network edge data centres
  • The limited capacity at network edge DCs will largely be used for edge applications
  • Most telecoms operators are taking a hybrid approach to building their edge
  • Conclusions and next steps
  • Appendix: Methodology

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