The evolution of data centres to accommodate edge computing
Halfway through the year, it is becoming increasingly clear that 2019 looks set to be the year of 'the edge' for telecoms and data center networks, as it makes its way into enterprise IT strategies driven by technological advancement.
One of these will be artificial intelligence (AI), which is gradually being adopted by businesses across the world and demands more compute with faster access, making edge an obvious solution.
IoT will also create new requirements as organisations collect vast amounts of data from IoT devices and apply machine learning (ML) and AI platforms for actionable intelligence.
The general consensus is that all of the above will move compute closer to where it will ultimately be used so that decisions can be made faster and from across the globe.
It is true that these new technologies are creating new demands for how data is processed.
However, this does not mean completely re-engineering data infrastructure around new micro-data centers at the edge; this, in fact, underestimates both the intelligence of devices and the capabilities of existing infrastructure.
The future of these new technologies is split-second decision-making at the device itself – whether on a computer in Paris, a mobile phone in Berlin, or in a Tesla on the motorway to Newcastle, where there's no risk from latency or interruption.
For example, oft-cited applications, such as autonomous vehicles, with their massive sensor arrays and ultra-severe latency requirements, need their intelligence to be local.
It needs to be in-vehicle in order to facilitate an instant decision – ie braking to avoid a pedestrian.
If remote processing was required, there would need to be a remote data center presence every tenth of a mile, and even that would risk an ill-timed loss of connection.
If there's an edge, it is in the autonomous vehicle itself and not in some nearby data center at the closest 5G small cell site.
The role of the core data center will be in handling this new and varied complexity – joining data from multiple sources and providing the compute for in-depth analysis and decision-making.
The Tesla on the motorway makes simple decisions in the car, while a high compute, highly-connected data center out of town is receiving data from the vehicle and hundreds of others, analysing that data and making complex decisions for all of those cars.
There will be instances where micro-data centers become vital.
For example, when a fire occurs, firefighters will arrive at the scene and must create an instant intelligent edge – the at-scene command centre.
The intelligent decision support must be at the scene or at the edge.
Data from body-worn sensors and other items of equipment must be integrated into a common operational picture for both commanders on-site and operators at the command centre.
Since they had no previous knowledge of where the situation would occur, the edge must be instantly created.
Adding to the challenge is the unknown on-site network availability and relying on a centralised location for decision support is impractical.
Ultimately, it is estimated that only 10% of IoT applications and their supporting workloads require a physical presence at the edge.
The remaining 90% can be sufficiently served from an existing metropolitan data center and co-location facilities.
Unless applications are in the 10%, there will be a need to take a complete view of the costs-versus-performance trade-offs when contemplating an intelligent IoT edge strategy.
In order to ensure that 5G, AI and autonomous vehicles continue to realise their potential in the latter half of 2019, data center providers need to focus not on creating a new edge, but on making connectivity, compute and interconnection more seamless and more available.
The task for data centers is in building a platform from which customers can respond rapidly to these changes.