Hyper-converged Infrastructure (HCI) and its relationship to the Internet of Things (IoT) is all about doing more with less — encouraging technological progress by way of simplification, for instance, HCI’s ability to streamline the deployment, management and scaling of data center resources with intelligent software.
Enterprise applications and the pace of modern business already threaten the lifespan of legacy IT design. With the advent of IoT, there are more devices, connections and integrations to manage than ever before. The future of IoT may sound daunting, but with an edge computing and IoT platform that is the right fit for your business, deploying planet-scale edge intelligence can be straightforward, cost-effective and a road to innovation for the enterprise.
Below are four best practices for using HCI to implement IoT projects at the edge.
Do More with IoT Data
Most organizations handle their oceans of data by processing it in the cloud — an approach that causes significant IT and business challenges like bandwidth congestion, lack of scalability, processing delays, limited security, compliance and privacy issues.
Traditional IT architectures weren’t designed to accommodate edge cloud workloads, and efforts to employ them in this new context can result in poor performance, disabling complexity and creating lost opportunities afforded by real-time intelligence at the edge. Utilizing a flexible IoT platform that has options to run as Virtual Machine (VM) on an HCI platform is key to adapting to ever-changing business requirements.
While IoT devices have been around for years, making sense of the data generated from these devices has not been a top priority for many organizations, mainly due to complexity and cost. With the right edge computing and IoT platform, however, deploying successful, planet-scale edge intelligence is an effective way to drive innovation for your enterprise.
Focus on Business Logic
Before using HCI to implement IoT projects at the edge, it’s critical to think of your existing business and how it is managed from edge to core to cloud.
Spend time focusing on business logic and less time managing infrastructure. The HCI platform in the data center should extend to the edge to simplify overall infrastructure deployments. Additionally, the HCI platform must extend capabilities for real-time analysis of sensor data via a VM which easily connects to your choice of public or private cloud. All important data will be locally processed with easy-to-use developer APIs, reusable data streams and a pluggable AI-based architecture to enable rapid global development of modern IoT applications. These APIs make it easy for developers to integrate into existing CI/CD pipelines. Some IoT models even allow you to bring customer AI models and apply them at the edge.
Improve Your Bottom Line
The ultimate goal of using HCI to implement IoT projects should be to improve your business’s bottom line by reducing overall costs. This can be done via automation — for instance, automating processes that control cameras and sensors to reduce infrastructure management time at the edge. Further, automating processes at the edge can result in lower enterprise risks. For example, predictive maintenance makes possible the ability to see an issue before it even occurs. Successfully implemented automation can even increase product yields, bringing lost dollars back into your business.
Clear examples of these benefits exist in the retail business. To improve its bottom line, a food service company, for instance, can use edge data to manage its inventory and minimize losses. Using HCI, it can leverage point-of-sale systems to ease the burden on managing traditionally complex infrastructure. A brick-and-mortar retail store can leverage connected devices to facilitate seamless integration between digital and physical sales streams — like digital signs that automatically update to match online pricing. The interconnected ecosystem of internet-enabled devices, sensors, data hubs, networks and microprocessors offers limitless opportunities to the industry.
Using HCI to implement IoT projects at the edge can help your business reach its monetary, efficiency and digital transformation goals. More importantly, implementing IoT in the best way possible for your specific enterprise is critical to achieving top results.
Run Legacy Applications with AI-Based Applications
It’s important to know that legacy applications can run alongside new artificial intelligence (AI) applications at the edge when using HCI architectures.
Existing applications are not always easily ported to new architectures like containers. For example, some point-of-sales systems require running as a VM, which is great on an HCI architecture per the built-in benefits the HCI solution offers. This reduces management complexity in remote environments where IT resources are scarce.
However, new AI-based applications that want to take advantage of containers at the edge, require an IoT platform that’s easy to deploy, manage, and scale and run alongside legacy applications. It’s important to have flexibility and choice to enable these new-age applications at the edge. They can learn from sensor or image data collected in real-time, and then pass this data to the cloud for deeper analysis. One key point is to think about how thousands of edge locations can send data to a centralized cloud to learn across locations.
Feature image via Pixabay.