Understanding Edge Computing Archetypes And Why It Matters To Your Smart Building – edge-computing.cioreview.com

Arunangshu Chattopadhyay, Director, Power Product Marketing, Vertiv Asia

Arunangshu Chattopadhyay, Director, Power Product Marketing, Vertiv Asia

It’s hard to imagine a smart city without towering skyscrapers and buildings that line the streets. Today, digital transformation is redefining the building construction industry through the use of intelligent solutions such as AI, robotics and internet of things (IoT). Many are now investing in these solutions to increase efficiency such as reduce energy consumption and meet sustainability goals and, in some cases, to comply with government regulations. In fact, according to Navigant Research, the revenue for intelligent building solutions is expected to grow to about $67.5 billion by 2027 at a compound annual growth rate (CAGR) of 18.1 percent.

With all these innovations transforming the building construction industry, there’s no denying that smart buildings make one of the building blocks of a smart city. A smart building is a confluence of several components that increase the structure’s intelligence. By collecting data and making continuous adjustments, the goal of a smart building is to increase efficiency within the facility, cut down on waste and optimize building operations.

Edge computing and smart buildings

With the huge amounts of data that’s being generated by IoT technologies, edge computing plays a critical role in smart building implementation. Simply put, edge computing places compute and storage away from core data centers to smaller, micro facilities closer to users, allowing data to be processed and stored much faster. This means that sensors, devices and other gateways are now given the power to act locally and not always rely on the core data center or cloud environment.

Edge computing architecture fits with the overall efficiency goals of a smart building. Not only does it reduce latency, but also produces significant savings as reaction time is faster and decisions can be made in real-time because data is processed faster as well. To better understand edge computing and its role in a smart ecosystem, experts from Vertiv have analyzed different use cases that comprise the edge ecosystem to develop a better understanding of these differences and their implications for the supporting infrastructure.

As a result of this analysis, we have identified four main archetypes for edge applications. Below I summarize the four archetypes and how they will affect your smart building deployment:

The four edge archetypes Data intensive

The Data Intensive Archetype represents use cases where the amount of data makes it impractical to transfer over the network directly to the cloud, or from the cloud to the point-ofuse, because of data volume, cost or bandwidth issues.

A prime example of the Data Intensive Archetype is the use of IoT networks to create smart homes, buildings, factories and cities. A 2018 survey by 451 Research and Vertiv found that while only 33 percent of the 700organizations surveyed had broadly deployed IoT, 56percent indicated that at least 25 percent of their IT capacity currently supports IoT. Despite IoT still being in its early stages, organizations are already struggling to manage the volume of data being generated.

In this case, rather than moving data closer to users, these applications must move the huge amounts of data generated CXO INSIGHTS by devices and systems at the source to a central location for processing. This will require the evolution of an edge-to-core network architecture.

Human-latency sensitive

The Human-Latency Sensitive Archetype covers use cases where services are optimized for human consumption. As the name suggests, speed is the defining characteristic of this archetype.

The challenge of human latency can be seen in the customer-experience optimization use case. In applications such as e-commerce, speed has a direct impact on the user experience; web sites that are optimized for speed using local infrastructure translate directly into increased page views and sales.

Machine-to-machine latency sensitive

The Machine-to-Machine Latency Sensitive Archetype covers use cases where services are optimized for machineto- machine consumption. Because machines can process data much faster than humans, speed is the defining characteristic of this archetype. The consequences for failing to deliver data at the required speeds can be even higher in this case than in the Human-Latency Sensitive Archetype.

Smart grid technology falls into this archetype. This technology is being deployed in the electrical distribution network to self-balance supply and demand and manage electricity use in a sustainable, reliable and economic manner. It enables distribution networks to self-heal, optimize for cost and manage intermittent power sources, assuming the right data is available at the right time. Other Machine-to-Machine Latency Sensitive applications include smart security systems that rely on image recognition, military war simulations, and real-time analytics.

Life critical

The Life Critical Archetype encompasses use cases that directly impact human health and safety. In these use cases, speed and reliability are paramount. Probably the best examples of the Life Critical Archetype are autonomous vehicles and drones, which provide great benefits when they operate as designed; however, if they make bad decisions, they can endanger human health.

Technology requirements for edge use cases

What is the impact of these archetypes to the existing data center infrastructure? It’s important to note that every archetype will require a local data hub, which provides storage and processing in close proximity to the source. In some cases, the local hub may be a free standing data center. More commonly, it will be a rack- or row-based system providing 30-300 kW of capacity in an integrated enclosure that can be installed in any environment.

These rack- and row-based enclosure systems integrate communication, compute and storage with appropriate power protection, environmental controls and physical security. For archetypes that require a high degree of availability, such as Machine-to-Machine Latency Sensitive and Life Critical, the local hub should include redundant backup power systems and be equipped to enable remote management and monitoring. Many uses cases will also require data encryption and other security features within the local hub.

By understanding these archetypes and the infrastructure needed to support them, decision-makers, including those in the building construction industry, can make better decisions regarding their IT infrastructure.

This UrIoTNews article is syndicated fromGoogle News