Edge Computing Architecture Guide

Edge computing is rapidly gaining popularity as more  organizations see the benefits of decentralizing data processing for Internet of Things (IoT) deployments, machine learning applications, operational technology (OT), AI and machine learning, and other edge use cases. This guide defines edge computing and edge-native applications, highlights a few key use cases, describes the typical components of an edge deployment, and provides additional resources for building your own edge computing architecture.

Table of Contents

What is edge computing? Edge-native applications Edge computing use cases Edge computing architecture design How to build an edge computing architecture with Nodegrid

What is edge computing?

The Open Glossary of Edge Computing defines it as deploying computing capabilities to the edges of a network to improve performance, reduce operating costs, and increase resilience. Edge computing reduces the number of network hops between data-generating devices and the applications that process and use that data, mitigating latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Image: A diagram showing the migration path from on-premises computing to edge computing, along with the associated level of security risk.

Edge-native applications

Edge-native applications are built from the ground up to harness edge computing’s unique capabilities while mitigating the limitations. They leverage some cloud-native principles, such as containers, microservices, and CI/CD (continuous integration/continuous delivery), with several key differences.

Edge-Native vs. Cloud-Native Applications

	Edge-Native	Cloud-Native
Topology	Distributed	Centralized
Compute	Real-time processing with limited resources	Deep processing with scalable resources
Data	Constantly changing and moving	Long-lived and at rest in a centralized location
Capabilities	Contextualized	Standardized
Location	Anywhere	Cloud data center

Source: Gartner

Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed. Crucially, they allow organizations to actually leverage their edge data in real-time, rather than just collecting it for later processing.

Edge computing use cases

Nearly every industry has potential use cases for edge computing, including:

Industry	Edge Computing Use Cases
Healthcare	Mitigating security, privacy, and HIPAA compliance concerns with local data processing Improving patient health outcomes with real-time alerts that don’t require Internet access Enabling emergency mobile medical intervention while reducing mistakes
Finance	Reducing security and regulatory risks through local computing and edge infrastructure isolation Getting fast, localized business insights to improve revenue and customer service Deploying AI-powered surveillance and security solutions without network bottlenecks
Energy	Enabling network access and real-time data processing for airgapped and isolated environments Improving efficiency with predictive maintenance recommendations and other insights Proactively identifying and remediating safety, quality, and compliance issues
Manufacturing	Getting real-time, data-driven insights to improve manufacturing efficiency and product quality Reducing the risk of confidential production data falling into the wrong hands in transit Ensuring continuous operations during network outages and other adverse events Using AI with computer vision to ensure worker safety and quality control of fabricated components/products
Utilities/Public Services	Using IoT technology to deliver better services, improve public safety, and keep communities connected Reducing the fleet management challenges involved in difficult deployment environments Aiding in disaster recovery and resilience with distributed redundant edge resources

To learn more about the specific benefits and uses of edge computing for each industry, read Distributed Edge Computing Use Cases.

Edge computing architecture design

An edge computing architecture consists of six major components:

Edge Computing Components	Description	Best Practices
Devices generating edge data	IoT devices, sensors, controllers, smartphones, and other devices that generate data at the edge	Use automated patch management to keep devices up-to-date and protect against known vulnerabilities
Edge software applications	Analytics, machine learning, and other software deployed at the edge to use edge data	Look for edge-native applications that easily integrate with other tools to prevent edge sprawl
Edge computing infrastructure	CPUs, GPUs, memory, and storage used to process data and run edge applications	Use vendor-neutral, multi-purpose hardware to reduce overhead and management complexity
Edge network infrastructure and logic	Wired and wireless connectivity, routing, switching, and other network functions	Deploy virtualized network functions and edge computing on common, vendor-neutral hardware
Edge security perimeter	Firewalls, endpoint security, web filtering, and other enterprise security functionality	Implement edge-centric security solutions like SASE and SSE to prevent network bottlenecks while protecting edge data
Centralized management and orchestration	An EMO (edge management and orchestration) platform used to oversee and conduct all edge operations	Use a cloud-based, Gen 3 out-of-band (OOB) management platform to ensure edge resilience and enable end-to-end automation

Click here to learn more about the infrastructure, networking, management, and security components of an edge computing architecture.

How to build an edge computing architecture with Nodegrid

Nodegrid is a Gen 3 out-of-band management platform that streamlines edge computing with vendor-neutral solutions and a centralized, cloud-based orchestrator.

Image: A diagram showing all the edge computing and networking capabilities provided by the Nodegrid Gate SR.

Nodegrid integrated services routers deliver all-in-one edge computing and networking functionality while taking up 1RU or less. A Nodegrid box like the Gate SR provides Ethernet and Serial switching, serial console/jumpbox management, WAN routing, wireless networking, and 5G/4G cellular for network failover or out-of-band management. It includes enough CPU, memory, and encrypted SSD storage to run edge computing workflows, and the x86-64bit Linux-based Nodegrid OS supports virtualized network functions, VMs, and containers for edge-native applications, even those from other vendors. The new Gate SR also comes with an embedded NVIDIA Jetson Orin NanoTM module featuring dual CPUs for EMO of AI workloads and infrastructure isolation.

Nodegrid SRs can also host SASE, SSE, and other security solutions, as well as third-party automation from top vendors like Redhat and Salt. Remote teams use the centralized, vendor-neutral ZPE Cloud platform (an on-premises version is available) to deploy, monitor, and orchestrate the entire edge architecture. Management, automation, and orchestration workflows occur over the Gen 3 OOB control plane, which is separated and isolated from the production network. Nodegrid OOB uses fast, reliable network interfaces like 5G cellular to enable end-to-end automation and ensure 24/7 remote access even during major outages, significantly improving edge resilience.

Streamline your edge deployment

The Nodegrid platform from ZPE Systems reduces the cost and complexity of building an edge computing architecture with vendor-neutral, all-in-one devices and centralized EMO. Request a free Nodegrid demo to learn more.

Click here to learn more!