At Hewlett Packard Enterprise (HPE), when discussing this major shift of computing to the edge, we often talk about the ‘Intelligent Edge’. We use this term to highlight the greater levels of – and need for – computing capacity at the edge of the network. In other words: micro datacentres built closer to population centres, closer to where data is generated, and closer to where real-time analysis is needed – this could be within a building, on a manufacturing factory floor, in a sports arena, or in your car, for example.
With a rise in connected Internet of Things (IoT) technologies and AI-enabled applications, both of which are producing valuable business insights and accelerating innovation cycles, data needs to be processed and analysed at the Edge. When data is collected, rather than transmitting that back to a datacentre or the Cloud to process, the Intelligent Edge triages and analyses that data locally. Not only does this mean that analysis can be performed more quickly, it also reduces decision latency because data does not have to traverse over a network – enabling real-time action and insight.
A good example of the need for Intelligent Edge technology is the rise of autonomous vehicles, which constantly generate high volumes of data on factors such as weather, driving conditions, GPS location and surrounding objects on the road. A self-driving car would use Edge computing to assess – in real time – whether an object that has triggered its proximity sensor is a carrier bag floating in the wind, or a pedestrian crossing the road. Subsequently, the car needs to make a judgement in a millisecond about whether to apply the brakes to avoid collision. This can only happen if the sensor data is processed, analysed and acted upon locally, not sent back and forth to a datacentre or Cloud. In the world of autonomous vehicles, a few milliseconds of latency can be the difference between safe driving and a potentially fatal collision.
It is worth emphasizing the huge amount of data we create at the Edge, that is ultimately digitalising our lives even further – for example, the sensors of just one self-driving car will generate four terabytes of data a day, the equivalent of more than 400 HD movies. The processing of this data at the Edge generates actionable insights, where big data can be acted upon to give more value to users. The more actionable insights generated, the smarter the systems, and the more intelligent the Edge.
At HPE we are at the forefront of the fast-growing market for Intelligent Edge solutions, in fact, the term was originally coined by Aruba; an HPE subsidiary company. One of the most urgent challenges our customers face is having too much data, with a lack of infrastructure to fully utilise it. That’s why we see a real need for Edge computing to address the vast data demands created by incoming new technology like autonomous vehicles, 5G, automated factories, smart buildings and others.
There are several good examples of how HPE have helped solve specific Edge related challenges, generating significant business value and improving user experiences:
With Tottenham Hotspur FC, we are helping build the world’s most technologically advanced stadium. As the official IT networking and Wireless Infrastructure Partner, our new wireless infrastructure will enable better connectivity for fans and a platform upon which connected fan experiences can be built. By using Edge computing throughout the stadium, data can be offloaded that would otherwise be crunched on centralised servers, and the venue is able to handle major spikes in demand.
Similarly, at the Mobile World Congress event in Barcelona this year we unveiled a new Edge product – the EdgeLine 8000 server – specifically designed for telecommunications service providers and compute-intensive edge tasks. The EL8000 has been designed to address the growing demand for 5G and the challenges faced by service providers in deploying it. Analysts forecast that more than 150 billion devices will be connected across the globe by 2025, most of which will be creating data in real time. To deliver new services that tap into this massive growth of real-time data, service providers must transform their telecommunications network edge and be able to process this data in real-time.
Increasingly businesses are looking for rich, connected experiences to generate significant value for customers and clients, and the way to enable this is to invest in building out intelligence at the network edge. This underlying network, based on high degrees of connectivity and high levels of security to enable data to be collected and understood, gives insight for operational efficiency and improving customer and employee engagement.
To capture the opportunity that the Intelligent Edge provides, every organization now needs to reassess their business, given the ease to access extraordinary computer capacity (intelligence, at the edge) and reimagine their business whilst embracing the Edge.