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Vertiv: The 4 primary edge archetypes and their tech requirements

18 Apr 18

Edge computing has long been a hot topic surrounding data centre circles, and now Vertiv has released a report shedding some light on the matter.

The global research-based analysis of network edge use cases identifies four main archetypes for edge applications and the technology required to support them.

Vertiv EMEA president Giordano Albertazzi says the opportunity around edge computing is significant - but so is the complexity.

“The aim of this research is to provide more clarity around key edge use cases and the implications for the design and operation of digital infrastructure,” says Albertazzi.

“By analysing what edge really means in all its different forms – from content distribution to autonomous vehicles – we can help our customers, partners and other stakeholders accelerate and focus their edge strategies."

Vertiv edge experts in collaboration with an independent third party consulting party identified data-centric sets of workload requirements for each edge use case and corresponding needs for performance, availability and security.

Specific performance requirements that were analysed included latency, availability, scalability and security, in conjunction with the need for encryption, authentication and regulatory compliance.

The four archetypes identified by Vertiv are:

Data Intensive – This includes use cases where the amount of data makes it impractical to transfer over the network directly to the cloud or from the cloud to point-of-use due to data volume, cost or bandwidth issues. Examples include smart cities, smart factories, smart homes/buildings, high-definition content distribution, high-performance computing, restricted connectivity, virtual reality, and oil and gas digitisation. The most widely used example is high-definition content delivery, where major content providers such as Amazon and Netflix actively partner with colocation providers to expand delivery networks to bring data-intensive streaming video closer to users to reduce costs and latency.

Human-Latency Sensitive – This archetype includes use cases where services are optimised for human consumption, and it is all about speed. Delayed data delivery negatively impacts a user’s technology experience, potentially reducing a retailer’s sales and profitability. Use cases include smart retail, augmented reality, website optimisation, and natural language processing.

Machine-to-Machine Latency Sensitive – Speed also is the defining characteristic of this archetype, which includes the arbitrage market, smart grid, smart security, real-time analytics, low-latency content distribution, and defence force simulation. Because machines are able to process data much faster than humans, the consequences for slow delivery are higher than in the Human-Latency Archetype. For example, delays in commodities and stock trading, where prices fluctuate within fractions of a second, may turn potential gains into losses.

Life Critical – This archetype encompasses use cases that directly impact human health and safety. Consequently, speed and reliability are vital. Use cases include smart transportation, digital health, connected/autonomous cars, autonomous robots, and drones. Autonomous vehicles, for example, must have updated data to operate safely, as is the case with drones that may be used for e-commerce and package delivery.

“As edge computing continues to evolve and expand, our goal is to bring clarity and simplicity to the critical infrastructure required to support the future of edge computing by viewing a wide range of edge applications through the lens of the most impactful archetypes,” says Vertiv global edge and integrated solutions vice president Martin Olsen.

“Vertiv is building on this initial phase of our research to define technology requirements for each archetype to help accelerate edge deployments and ensure local infrastructure provides the security, speed and availability a particular application requires.”