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Edge Computing Platforms: Insights from Gartner’s 2024 Market Guide

by | Nov 11, 2024 | Application Hosting, Consolidation, Edge Computing, EdgeOps, Failover Connectivity, Improve Network Security, Increase Productivity, Minimize Impact of Disruptions, Modernize Legacy Environments, Monitoring & Reporting, Network Automation, Out of Band Management, Power Management, Remote Network Management, SD-Branch, SD-WAN, Secure Access Service Edge (SASE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP)

Interlocking cogwheels containing icons of various edge computing examples are displayed in front of racks of servers

Edge computing allows organizations to process data close to where it’s generated, such as in retail stores, industrial sites, and smart cities, with the goal of improving operational efficiency and reducing latency. However, edge computing requires a platform that can support the necessary software, management, and networking infrastructure. Let’s explore the 2024 Gartner Market Guide for Edge Computing, which highlights the drivers of edge computing and offers guidance for organizations considering edge strategies.

What is an Edge Computing Platform (ECP)?

Edge computing moves data processing close to where it’s generated. For bank branches, manufacturing plants, hospitals, and others, edge computing delivers benefits like reduced latency, faster response times, and lower bandwidth costs. An Edge Computing Platform (ECP) provides the foundation of infrastructure, management, and cloud integration that enable edge computing. The goal of having an ECP is to allow many edge locations to be efficiently operated and scaled with minimal, if any, human touch or physical infrastructure changes.

Before we describe ECPs in detail, it’s important to first understand why edge computing is becoming increasingly critical to IT and what challenges arise as a result.

What’s Driving Edge Computing, and What Are the Challenges?

Here are the five drivers of edge computing described in Gartner’s report, along with the challenges that arise from each:

1. Edge Diversity

Every industry has its unique edge computing requirements. For example, manufacturing often needs low-latency processing to ensure real-time control over production, while retail might focus on real-time data insights to deliver hyper-personalized customer experiences.

Challenge: Edge computing solutions are usually deployed to address an immediate need, without taking into account the potential for future changes. This makes it difficult to adapt to diverse and evolving use cases.

2. Ongoing Digital Transformation

Gartner predicts that by 2029, 30% of enterprises will rely on edge computing. Digital transformation is catalyzing its adoption, while use cases will continue to evolve based on emerging technologies and business strategies.

Challenge: This rapid transformation means environments will continue to become more complex as edge computing evolves. This complexity makes it difficult to integrate, manage, and secure the various solutions required for edge computing.

3. Data Growth

The amount of data generated at the edge is increasing exponentially due to digitalization. Initially, this data was often underutilized (referred to as the “dark edge”), but businesses are now shifting towards a more connected and intelligent edge, where data is processed and acted upon in real time.

Challenge: Enormous volumes of data make it difficult to efficiently manage data flows and support real-time processing without overwhelming the network or infrastructure.

4. Business-Led Requirements

Automation, predictive maintenance, and hyper-personalized experiences are key business drivers pushing the adoption of edge solutions across industries.

Challenge: Meeting business requirements poses challenges in terms of ensuring scalability, interoperability, and adaptability.

5. Technology Focus

Emerging technologies such as AI/ML are increasingly deployed at the edge for low-latency processing, which is particularly useful in manufacturing, defense, and other sectors that require real-time analytics and autonomous systems.

Challenge: AI and ML make it difficult for organizations to determine how to strike a balance between computing power and infrastructure costs, without sacrificing security.

What Features Do Edge Computing Platforms Need to Have?

To address these challenges, here’s a brief look at three core features that ECPs need to have according to Gartner’s Market Guide:

  1. Edge Software Infrastructure: Support for edge-native workloads and infrastructure, including containers and VMs. The platform must be secure by design.
  2. Edge Management and Orchestration: Centralized management for the full software stack, including orchestration for app onboarding, fleet deployments, data storage, and regular updates/rollbacks.
  3. Cloud Integration and Networking: Seamless connection between edge and cloud to ensure smooth data flow and scalability, with support for upstream and downstream networking.

A simple diagram showing the computing and networking capabilities that can be delivered via Edge Management and Orchestration.

Image: A simple diagram showing the computing and networking capabilities that can be delivered via Edge Management and Orchestration.

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How ZPE Systems’ Nodegrid Platform Addresses Edge Computing Challenges

ZPE Systems’ Nodegrid is a Secure Service Delivery Platform that meets these needs. Nodegrid covers all three feature categories outlined in Gartner’s report, allowing organizations to host and manage edge computing via one platform. Not only is Nodegrid the industry’s most secure management infrastructure, but it also features a vendor-neutral OS, hypervisor, and multi-core Intel CPU to support necessary containers, VMs, and workloads at the edge. Nodegrid follows isolated management best practices that enable end-to-end orchestration and safe updates/rollbacks of global device fleets. Nodegrid integrates with all major cloud providers, and also features a variety of uplink types, including 5G, Starlink, and fiber, to address use cases ranging from setting up out-of-band access, to architecting Passive Optical Networking.

Here’s how Nodegrid addresses the five edge computing challenges:

1. Edge Diversity: Adapting to Industry-Specific Needs

Nodegrid is built to handle diverse requirements, with a flexible architecture that supports containerized applications and virtual machines. This architecture enables organizations to tailor the platform to their edge computing needs, whether for handling automated workflows in a factory or data-driven customer experiences in retail.

2. Ongoing Digital Transformation: Supporting Continuous Growth

Nodegrid supports ongoing digital transformation by providing zero-touch orchestration and management, allowing for remote deployment and centralized control of edge devices. This enables teams to perform initial setup of all infrastructure and services required for their edge computing use cases. Nodegrid’s remote access and automation provide a secure platform for keeping infrastructure up-to-date and optimized without the need for on-site staff. This helps organizations move much of their focus away from operations (“keeping the lights on”), and instead gives them the agility to scale their edge infrastructure to meet their business goals.

3. Data Growth: Enabling Real-Time Data Processing

Nodegrid addresses the challenge of exponential data growth by providing local processing capabilities, enabling edge devices to analyze and act on data without relying on the cloud. This not only reduces latency but also enhances decision-making in time-sensitive environments. For instance, Nodegrid can handle the high volumes of data generated by sensors and machines in a manufacturing plant, providing instant feedback for closed-loop automation and improving operational efficiency.

4. Business-Led Requirements: Tailored Solutions for Industry Demands

Nodegrid’s hardware and software are designed to be adaptable, allowing businesses to scale across different industries and use cases. In manufacturing, Nodegrid supports automated workflows and predictive maintenance, ensuring equipment operates efficiently. In retail, it powers hyperpersonalization, enabling businesses to offer tailored customer experiences through edge-driven insights. The vendor-neutral Nodegrid OS integrates with existing and new infrastructure, and the Net SR is a modular appliance that allows for hot-swapping of serial, Ethernet, computing, storage, and other capabilities. Organizations using Nodegrid can adapt to evolving use cases without having to do any heavy lifting of their infrastructure.

5. Technology Focus: Supporting Advanced AI/ML Applications

Emerging technologies such as AI/ML require robust edge platforms that can handle complex workloads with low-latency processing. Nodegrid excels in environments where real-time analytics and autonomous systems are crucial, offering high-performance infrastructure designed to support these advanced use cases. Whether processing data for AI-driven decision-making in defense or enabling real-time analytics in industrial environments, Nodegrid provides the computing power and scalability needed for AI/ML models to operate efficiently at the edge.

Read Gartner’s Market Guide for Edge Computing Platforms

As businesses continue to deploy edge computing solutions to manage increasing data, reduce latency, and drive innovation, selecting the right platform becomes critical. The 2024 Gartner Market Guide for Edge Computing Platforms provides valuable insights into the trends and challenges of edge deployments, emphasizing the need for scalability, zero-touch management, and support for evolving workloads.

Click below to download the report.

Download Market Guide

Get a Demo of Nodegrid’s Secure Service Delivery

Our engineers are ready to walk you through the software infrastructure, edge management and orchestration, and cloud integration capabilities of Nodegrid. Use the form to set up a call and get a hands-on demo of this Secure Service Delivery Platform.

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Comparing Edge Security Solutions

by | Jul 10, 2024 | Application Hosting, EdgeOps, Improve Network Security, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Secure Access Service Edge (SASE), Security Service Edge (SSE), Streamline Deployments, Vendor Neutral Platform

A user at an edge site with a virtual overlay of SASE and related edge security concepts
The continuing trend of enterprise network decentralization to support Internet of Things (IoT) deployments, automation, and edge computing is resulting in rapid growth for the edge security market. Recent research predicts it will reach $82.4 billion by 2031 at a compound annual growth rate (CAGR) of 19.7% from 2024.

Edge security solutions decentralize the enterprise security stack, delivering key firewall capabilities to the network’s edges. This prevents companies from funneling all edge traffic through a centralized data center firewall, reducing latency and improving overall performance.

This guide compares the most popular edge security solutions and offers recommendations for choosing the right vendor for your use case.

Executive summary

There are six single-vendor SASE solutions offering the best combination of features and capabilities for their targeted use cases.
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Single-Vendor SASE Product

Key Takeaways

Palo Alto Prisma SASE

Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Zscaler Zero Trust SASE

Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and feature roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology (OT) deployments.

Netskope ONE

Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Cisco

Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Forcepoint ONE

Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging. 

Fortinet FortiSASE

FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

The best edge security solution for Gen 3 out-of-band (OOB) management, which is critical for infrastructure isolation, resilience, and operational efficiency, is Nodegrid from ZPE Systems. Nodegrid provides secure hardware and software to host other vendors’ tools on a secure, Gen 3 OOB network. It creates a control plane for edge infrastructure that’s completely isolated from breaches on the production network and consolidates an entire edge networking stack into a single solution. Disclaimer: This comparison was written by a third party in collaboration with ZPE Systems using publicly available information gathered from data sheets, admin guides, and customer reviews on sites like Gartner Peer Insights, as of 6/09/2024. Please email us if you have corrections or edits, or want to review additional attributes, at matrix@zpesystems.com.

What are edge security solutions?

Edge security solutions primarily fall into one (or both) of two categories:

  • Security Service Edge (SSE) solutions deliver core security features as a managed service. SSE does not come with any networking capabilities, so companies still need a way to securely route edge traffic through the (often cloud-based) security stack. This usually involves software-defined wide area networking (SD-WAN), which was traditionally a separate service that had to be integrated with the SSE stack.
  • Secure Access Service Edge (SASE) solutions package SSE together with SD-WAN, preventing companies from needing to deploy and manage multiple vendor solutions.

All the top SSE providers now offer fully integrated SASE solutions with SD-WAN. SASE’s main tech stack is in the cloud, but organizations must install SD-WAN appliances at each branch or edge data center. SASE also typically uses software agents deployed at each site and, in some cases, on all edge devices. Some SASE vendors also sell physical appliances, while others only provide software licenses for virtualized SD-WAN solutions. A third category of edge security solutions offers a secure platform to run other vendors’ SD-WAN and SASE software. These solutions also provide an important edge security capability: management network isolation. This feature ensures that ransomware, viruses, and malicious actors can’t jump from compromised IoT devices to the management interfaces used to control vital edge infrastructure.

Comparing edge security solutions

Palo Alto Prisma SASE

A screenshot from the Palo Alto Prisma SASE solution. Palo Alto Prisma was named a Leader in Gartner’s 2023 SSE Magic Quadrant for its ability to deliver best-in-class security features. Prisma SASE is a cloud-native, AI-powered solution with the industry’s first native Autonomous Digital Experience Management (ADEM) service. Prisma’s ADEM has built-in AIOps for automatic incident detection, diagnosis, and remediation, as well as self-guided remediation to streamline the end-user experience. Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Palo Alto Prisma SASE Capabilities:

  • Zero Trust Network Access (ZTNA) 2.0 – Automated app discovery, fine-grained access controls, continuous trust verification, and deep security inspection.
  • Cloud Secure Web Gateway (SWG) – Inline visibility and control of web and SaaS traffic.
  • Next-Gen Cloud Access Security Broker (CASB) – Inline and API-based security controls and contextual policies.
  • Remote Browser Isolation (RBI) – Creates a secure isolation channel between users and remote browsers to prevent web threats from executing on their devices.
  • App acceleration – Application-aware routing to improve “first-mile” connection performance.
  • Prisma Access Browser – Policy management for edge devices.
  • Firewall as a Service (FWaaS) – Advanced threat protection, URL filtering, DNS security, and other next-generation firewall (NGFW) features.
  • Prisma SD-WAN – Elastic networks, app-defined fabric, and Zero Trust security.

Zscaler Zero Trust SASE

Zscaler is another 2023 SSE Magic Quadrant Leader offering a robust single-vendor SASE solution based on its Zero Trust ExchangeTM platform. Zscaler SASE uses artificial intelligence to boost its SWG, firewall, and DEM capabilities. It also offers IoT device management and OT privileged access management, allowing companies to secure unmanaged devices and provide secure remote access to industrial automation systems and other operational technology. Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and future roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology deployments.

Zscaler Zero Trust SASE Capabilities:

  • Zscaler Internet AccessTM (ZIA) – SWG cyberthreat protection and zero-trust access to SaaS apps and the web.
  • Zscaler Private AccessTM (ZPA) – ZTNA connectivity to private apps and OT devices.
  • Zscaler Digital ExperienceTM (ZDX) –  DEM with Microsoft Copilot AI to streamline incident management.
  • Zscaler Data Protection – CASB/DLP secures edge data across platforms.
  • IoT device visibility – IoT device, server, and unmanaged user device discovery, monitoring, and management.
  • Privileged OT access – Secure access management for third-party vendors and remote user connectivity to OT systems.
  • Zero Trust SD-WAN – Works with the Zscaler Zero Trust Exchange platform to secure edge and branch traffic.

Netskope ONE

Netskope is the only 2023 SSE Magic Quadrant Leader to offer a single-vendor SASE targeted to mid-market companies with smaller budgets as well as larger enterprises. The Netskope ONE platform provides a variety of security features tailored to different deployment sizes and requirements, from standard SASE offerings like ZTNA and CASB to more advanced capabilities such as AI-powered threat detection and user and entity behavior analytics (UEBA). Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Netskope ONE Capabilities:

  • Next-Gen SWG – Protection for cloud services, applications, websites, and data.
  • CASB – Security for both managed and unmanaged cloud applications.
  • ZTNA Next –  ZTNA with integrated software-only endpoint SD-WAN.
  • Netskope Cloud Firewall (NCF) – Outbound network traffic security across all ports and protocols.
  • RBI – Isolation for uncategorized and risky websites.
  • SkopeAI – AI-powered threat detection, UEBA, and DLP
  • Public Cloud Security – Visibility, control, and compliance for multi-cloud environments.
  • Advanced analytics – 360-degree risk analysis.
  • Cloud Exchange – Multi-cloud integration tools.
  • DLP – Sensitive data discovery, monitoring, and protection.
  • Device intelligence – Zero trust device discovery, risk assessment, and management.
  • Proactive DEM – End-to-end visibility and real-time insights.
  • SaaS security posture management – Continuous monitoring and enforcement of SaaS security settings, policies, and best practices.
  • Borderless SD-WAN – Zero trust connectivity for edge, branch, cloud, remote users, and IoT devices.

Cisco

Cisco is one of the only edge security vendors to offer SASE as a managed service for companies with lean IT operations and a lack of edge networking experience. Cisco Secure Connect SASE-as-a-service includes all the usual SSE capabilities, such as ZTNA, SWG, and CASB, as well as native Meraki SD-WAN integration and a generative AI assistant. Cisco also provides traditional SASE by combining Cisco Secure Access SSE – which includes the Cisco Umbrella Secure Internet Gateway (SIG) – with Catalyst SD-WAN. Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Cisco Secure Connect SASE-as-a-Service Capabilities:

  • Clientless ZTNA
  • Client-based Cisco AnyConnect secure remote access
  • SWG
  • Cloud-delivered firewall
  • DNS-layer security
  • CASB
  • DLP
  • SAML user authentication
  • Generative AI assistant
  • Network interconnect intelligent routing
  • Native Meraki SD-WAN integration
  • Unified management

Cisco Secure Access SASE Capabilities

  • ZTNA 
  • SWG
  • CASB
  • DLP
  • FWaaS
  • DNS-layer security
  • Malware protection
  • RBI
  • Catalyst SD-WAN

Forcepoint ONE

A screenshot from the Forcepoint ONE SASE solution. Forcepoint ONE is a cloud-native single-vendor SASE solution placing a heavy emphasis on edge and multi-cloud visibility. Forcepoint ONE aggregates live telemetry from all Forcepoint security solutions and provides visualizations, executive summaries, and deep insights to help companies improve their security posture. Forcepoint also offers what they call data-first SASE, focusing on protecting data across edge and cloud environments while enabling seamless access for authorized users from anywhere in the world. Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging.

Forcepoint ONE Capabilities:

  • CASB – Access control and data security for over 800,000 cloud apps on managed and unmanaged devices.
  • ZTNA – Secure remote access to private web apps.
  • SWG – Includes RBI, content disarm & reconstruction (CDR), and a cloud firewall.
  • Data Security – A cloud-native DLP to help enforce compliance across clouds, apps, emails, and endpoints.
  • Insights – Real-time analysis of live telemetry data from Forcepoint ONE security products.
  • FlexEdge SD-WAN – Secure access for branches and remote edge sites.

Fortinet FortiSASE

Fortinet’s FortiSASE platform combines feature-rich, AI-powered NGFW security functionality with SSE, digital experience monitoring, and a secure SD-WAN solution. Fortinet’s SASE offering includes the FortiGate NGFW delivered as a service, providing access to FortiGuard AI-powered security services like antivirus, application control, OT security, and anti-botnet protection. FortiSASE also integrates with the FortiMonitor DEM SaaS platform to help organizations optimize endpoint application performance. FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

Fortinet FortiSASE Capabilities:

  • Antivirus – Protection from the latest polymorphic attacks, ransomware, viruses, and other threats.
  • DLP – Prevention of intentional and accidental data leaks.
  • AntiSpam – Multi-layered spam email filtering.
  • Application Control – Policy creation and management for enterprise and cloud-based applications.
  • Attack Surface Security – Security Fabric infrastructure assessments based on major security and compliance frameworks.
  • CASB – Inline and API-based cloud application security.
  • DNS Security – DNS traffic visibility and filtering.
  • IPS – Deep packet inspection (DPI) and SSL inspection of network traffic.
  • OT Security – IPS for OT systems including ICS and SCADA protocols.
  • AI-Based Inline Malware Prevention – Real-time protection against zero-day exploits and sophisticated, novel threats.
  • URL Filtering – AI-powered behavior analysis and correlation to block malicious URLs.
  • Anti-Botnet and C2 – Prevention of unauthorized communication attempts from compromised remote servers.
  • FortiMonitor DEM – SaaS-based digital experience monitoring.
  • Secure SD-WAN – On-premises and cloud-based SD-WAN integrated into the same OS as the SSE security solutions.

Edge isolation and security with ZPE Nodegrid

The Nodegrid platform from ZPE Systems is a different type of edge security solution, providing secure hardware and software to host other vendors’ tools on a secure, Gen 3 out-of-band (OOB) management network. Nodegrid integrated branch services routers use alternative network interfaces (including 5G/4G LTE) and serial console technology to create a control plane for edge infrastructure that’s completely isolated from breaches on the production network. It uses hardware security features like secure boot and geofencing to prevent physical tampering, and it supports strong authentication methods and SAML integrations to protect the management network. A screenshot from the Forcepoint ONE SASE solution. Nodegrid’s OOB also ensures remote teams have 24/7 access to manage, troubleshoot, and recover edge deployments even during a major network outage or ransomware infection. Plus, Nodegrid’s ability to host Guest OS, including Docker containers and VNFs, allows companies to consolidate an entire edge networking stack in a single platform. Nodegrid devices like the Gate SR with Nvidia Jetson Nano can even run edge computing and AI/ML workloads alongside SASE. .

ZPE Nodegrid Edge Security Capabilities

  • Vendor-neutral platform – Hosting for third-party applications and services, including Docker containers and virtualized network functions.
  • Gen 3 OOB – Management interface isolation and 24/7 remote access during outages and breaches.
  • Branch networking – Routing and switching, VNFs, and software-defined branch networking (SD-Branch).
  • Secure boot – Password-protected BIO/Grub and signed software.
  • Latest kernel & cryptographic modules – 64-bit OS with current encryption and frequent security patches.
  • SSO with SAML, 2FA, & remote authentication – Support for Duo, Okta, Ping, and ADFS.
  • Geofencing – GPS tracking with perimeter crossing detection.
  • Fine-grain authorization – Role-based access control.
  • Firewall – Native IPSec & Fail2Ban intrusion prevention and third-party extensibility.
  • Tampering protection – Configuration checksum and change detection with a configuration ‘reset’ button.
  • TPM encrypted storage – Software encryption for SSD hardware storage.

Deploy edge security solutions on the vendor-neutral Nodegrid OOB platform

Nodegrid’s secure hardware and vendor-neutral OS make it the perfect platform for hosting other vendors’ SSE, SD-WAN, and SASE solutions. Reach out today to schedule a free demo.

Schedule a Demo

Applications of Edge Computing

by | Jul 9, 2024 | Application Hosting, Edge Computing, Increase Productivity, Modernize Legacy Environments, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, SD-Branch, SD-WAN, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP)

A healthcare worker presents various edge computing concepts to highlight some of the applications of edge computing

The edge computing market is huge and continuing to grow. A recent study projected that spending on edge computing will reach $232 billion in 2024. Organizations across nearly every industry are taking advantage of edge computing’s real-time data processing capabilities to get immediate business insights, respond to issues at remote sites before they impact operations, and much more. This blog discusses some of the applications of edge computing for industries like finance, retail, and manufacturing, and provides advice on how to get started.

What is edge computing?

Edge computing involves decentralizing computing capabilities and moving them to the network’s edges. Doing so reduces the number of network hops between data sources and the applications that process and use that data, which mitigates latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Learn more about edge computing vs cloud computing or edge computing vs on-premises computing.

Edge computing often uses edge-native applications that are built from the ground up to harness edge computing’s unique capabilities and overcome its limitations. Edge-native applications leverage some cloud-native principles, such as containers, microservices, and CI/CD. However, unlike cloud-native apps, they’re designed to process transient, ephemeral data in real time with limited computational resources. Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed.
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Applications of edge computing

Industry

Applications

Financial services

  • Mitigate security and compliance risks of off-site data transmission

  • Gain real-time customer and productivity insights

  • Analyze surveillance footage in real-time

Industrial manufacturing

  • Monitor and respond to OT equipment issues in real-time

  • Create more efficient maintenance schedules

  • Prevent network outages from impacting production

Retail operations

  • Enhance the in-store customer experience

  • Improve inventory management and ordering

  • Aid loss prevention with live surveillance analysis

Healthcare

  • Monitor and respond to patient health issues in real-time

  • Mitigate security and compliance risks by keeping data on-site

  • Reduce networking requirements for wearable sensors

Oil, gas, & mining

  • Ensure continuous monitoring even during network disruptions

  • Gain real-time safety, maintenance, and production recommendations

  • Enable remote troubleshooting and recovery of IT systems

AI & machine learning

  • Reduce the costs and risks of high-volume data transmissions

  • Unlock near-instantaneous AI insights at the edge

  • Improve AIOps efficiency and resilience at branches

Financial services

The financial services industry collects a lot of edge data from bank branches, web and mobile apps, self-service ATMs, and surveillance systems. Many firms feed this data into AI/ML-powered data analytics software to gain insights into how to improve their services and generate more revenue. Some also use AI-powered video surveillance systems to analyze video feeds and detect suspicious activity. However, there are enormous security, regulatory, and reputational risks involved in transmitting this sensitive data to the cloud or an off-site data center.

Financial institutions can use edge computing to move data analytics applications to branches and remote PoPs (points of presence) to help mitigate the risks of transmitting data off-site. Additionally, edge computing enables real-time data analysis for more immediate and targeted insights into customer behavior, branch productivity, and security. For example, AI surveillance software deployed at the edge can analyze live video feeds and alert on-site security personnel about potential crimes in progress.

Industrial manufacturing

Many industrial manufacturing processes are mostly (if not completely) automated and overseen by operational technology (OT), such as supervisory control and data acquisition systems (SCADA). Logs from automated machinery and control systems are analyzed by software to monitor equipment health, track production costs, schedule preventative maintenance, and perform quality assurance (QA) on components and products. However, transferring that data to the cloud or centralized data center increases latency and creates security risks.

Manufacturers can use edge computing to analyze OT data in real time, gaining faster insights and catching potential issues before they affect product quality or delivery schedules. Edge computing also allows industrial automation and monitoring processes to continue uninterrupted even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event in the region. Edge resilience can be further improved by deploying an out-of-band (OOB) management solution like Nodegrid that enables control plane/data plane isolation (also known as isolated management infrastructure), as this will give remote teams a lifeline to access and recover OT systems.

Retail operations

In the age of one-click online shopping, the retail industry has been innovating with technology to enhance the in-store experience, improve employee productivity, and keep operating costs down. Retailers have a brief window of time to meet a customer’s needs before they look elsewhere, and edge computing’s ability to leverage data in real time is helping address that challenge. For example, some stores place QR codes on shelves that customers can scan if a product is out of stock, alerting a nearby representative to provide immediate assistance.

Another retail application of edge computing is enhanced inventory management. An edge computing solution can make ordering recommendations based on continuous analysis of purchasing patterns over time combined with real-time updates as products are purchased or returned. Retail companies, like financial institutions, can also use edge AI/ML solutions to analyze surveillance data and aid in loss prevention.

Healthcare

The healthcare industry processes massive amounts of data generated by medical equipment like insulin pumps, pacemakers, and imaging devices. Patient health data can’t be transferred over the open Internet, so getting it to the cloud or data center for analysis requires funneling it through a central firewall via MPLS (for hospitals, clinics, and other physical sites), overlay networks, or SD-WAN (for wearable sensors and mobile EMS devices). This increases the number of network hops and creates a traffic bottleneck that prevents real-time patient monitoring and delays responses to potential health crises.

Edge computing for healthcare allows organizations to process medical data on the same local network, or even the same onboard chip, as the sensors and devices that generate most of the data. This significantly reduces latency and mitigates many of the security and compliance challenges involved in transmitting regulated health data offsite. For example, an edge-native application running on an implanted heart-rate monitor can operate without a network connection much of the time, providing the patient with real-time alerts so they can modify their behavior as needed to stay healthy. If the app detects any concerning activity, it can use multiple cellular and ATT FirstNet connections to alert the cardiologist without exposing any private patient data.

Oil, gas, & mining

Oil, gas, and other mining operations use IoT sensors to monitor flow rates, detect leaks, and gather other critical information about equipment deployed in remote sites, drilling rigs, and offshore platforms all over the world. Drilling rigs are often located in extremely remote or even human-inaccessible locations, so ensuring reliable communications with monitoring applications in the cloud or data center can be difficult. Additionally, when networks or systems fail, it can be time-consuming and expensive – not to mention risky – to deploy IT teams to fix the issue on-site.

The energy and mining industries can use edge computing to analyze data in real time even in challenging deployment environments. For example, companies can deploy monitoring software on cellular-enabled edge computing devices to gain immediate insights into equipment status, well logs, borehole logs, and more. This software can help establish more effective maintenance schedules, uncover production inefficiencies, and identify potential safety issues or equipment failures before they cause larger problems. Edge solutions with OOB management also allow IT teams to fix many issues remotely, using alternative cellular interfaces to provide continuous access for troubleshooting and recovery.

AI & machine learning

Artificial intelligence (AI) and machine learning (ML) have broad applications across many industries and use cases, but they’re all powered by data. That data often originates at the network’s edges from IoT devices, equipment sensors, surveillance systems, and customer purchases. Securely transmitting, storing, and preparing edge data for AI/ML ingestion in the cloud or centralized data center is time-consuming, logistically challenging, and expensive. Decentralizing AI/ML’s computational resources and deploying them at the edge can significantly reduce these hurdles and unlock real-time capabilities.

For example, instead of deploying AI on a whole rack of GPUs (graphics processing units) in a central data center to analyze equipment monitoring data for all locations, a manufacturing company could use small edge computing devices to provide AI-powered analysis for each individual site. This would reduce bandwidth costs and network latency, enabling near-instant insights and providing an accelerated return on the investment into artificial intelligence technology.

AIOps can also be improved by edge computing. AIOps solutions analyze monitoring data from IT devices, network infrastructure, and security solutions and provide automated incident management, root-cause analysis, and simple issue remediation. Deploying AIOps on edge computing devices enables real-time issue detection and response. It also ensures continuous operation even if an ISP outage or network failure cuts off access to the cloud or central data center, helping to reduce business disruptions at vital branches and other remote sites.

Getting started with edge computing

The edge computing market has focused primarily on single-use-case solutions designed to solve specific business problems, forcing businesses to deploy many individual applications across the network. This piecemeal approach to edge computing increases management complexity and risk while decreasing operational efficiency.

The recommended approach is to use a centralized edge management and orchestration (EMO) platform to monitor and control edge computing operations. The EMO should be vendor-agnostic and interoperate with all the edge computing devices and edge-native applications in use across the organization. The easiest way to ensure interoperability is to use vendor-neutral edge computing platforms to run edge-native apps and AI/ML workflows.

For example, the Nodegrid platform from ZPE Systems provides the perfect vendor-neutral foundation for edge operations. Nodegrid integrated branch services routers like the Gate SR with integrated Nvidia Jetson Nano use the open, Linux-based Nodegrid OS, which can host Docker containers and edge-native applications for third-party AI, ML, data analytics, and more. These devices use out-of-band management to provide 24/7 remote visibility, management, and troubleshooting access to edge deployments, even in challenging environments like offshore oil rigs. Nodegrid’s cloud-based or on-premises software provides a single pane of glass to orchestrate operations at all edge computing sites.

Streamline your edge computing deployment with Nodegrid

The vendor-neutral Nodegrid platform can simplify all applications of edge computing with easy interoperability, reduced hardware overhead, and centralized edge management and orchestration. Schedule a Nodegrid demo to learn more.
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Edge Computing Examples

by | Jun 21, 2024 | Application Hosting, Edge Computing, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Security Service Edge (SSE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP)

Interlocking cogwheels containing icons of various edge computing examples are displayed in front of racks of servers

The edge computing market is growing fast, with experts predicting edge computing spending to reach almost $350 billion in 2027. Companies use edge computing to leverage data from Internet of Things (IoT) sensors and other devices at the periphery of the network in real-time, unlocking faster insights, accelerating ROIs for artificial intelligence and machine learning investments, and much more. This blog highlights 7 edge computing examples from across many different industries and provides tips and best practices for each use case.

What is edge computing?

Edge computing involves moving compute capabilities – processing units, RAM, storage, data analysis software, etc. – to the network’s edges. This allows companies to analyze or otherwise use edge data in real-time, without transmitting it to a central data center or the cloud.

Edge Computing Learning Center

Edge computing shortens the physical and logical distance between data-generating devices and the applications that use that data, which reduces bandwidth costs and network latency while simplifying many aspects of data security and compliance.

7 Edge computing examples

Below are 7 examples of how organizations use edge computing, along with best practices for overcoming the typical challenges involved in each use case. Click the links in the table for more information about each example.

Examples Best Practices
Monitoring inaccessible equipment in the oil & gas industry Use a vendor-neutral edge computing & networking platform to reduce the tech stack at each site.
Remotely managing and securing automated Smart buildings Isolate the management interfaces for automated building management systems from production to reduce risk.
Analyzing patient health data generated by mobile devices Protect patient privacy with strong hardware roots-of-trust, Zero Trust Edge integrations, and control plane/data plane separation.
Reducing latency for live streaming events and online gaming Use all-in-one, vendor-neutral devices to minimize hardware overhead and enable cost-effective scaling.
Improving performance and business outcomes for AI/ML Streamline operations by using a vendor-neutral platform to remotely monitor and orchestrate edge AI/ML deployments.
Enhancing remote surveillance capabilities at banks and ATMs Isolate the management interfaces for all surveillance systems using Gen 3 OOB to prevent compromise.
Extending data analysis to agriculture sites with limited Internet access Deploy edge gateway routers with environmental sensors to monitor operating conditions and prevent equipment failures.

1. Monitoring and managing inaccessible equipment in the oil and gas industry

The oil and gas industry uses IoT sensors to monitor flow rates, detect leaks, and gather other critical information about human-inaccessible equipment and operations. With drilling rigs located offshore and in extremely remote locations, ensuring reliable internet access to communicate with cloud-based or on-premises monitoring applications can be tricky. Dispatching IT teams to diagnose and repair issues is also costly, time-consuming, and risky. Edge computing allows oil and gas companies to process data on-site and in real-time, so safety issues and potential equipment failures are caught and remediated as soon as possible, even when Internet access is spotty.

Best practice: Use a vendor-neutral edge computing & networking platform like the Nodegrid Gate SR to reduce the tech stack at each site. The Gate SR can host other vendors’ software for SD-WAN, Secure Access Service Edge (SASE), equipment monitoring, and more. It also provides out-of-band (OOB) management and built-in cellular failover to improve network availability and resilience. Read this case study to learn more.

2. Remotely managing and securing fully automated Smart buildings

Smart buildings use IoT sensors to monitor and control building functions such as HVAC, lighting, power, and security. Property management companies and facilities departments use data analysis software to automatically determine optimal conditions, respond to issues, and alert technicians when emergencies occur. Edge computing allows these automated processes to respond to changing conditions in real-time, reducing the need for on-site personnel and improving operational efficiency.

Best practice: Keep the management interfaces for automated building management systems isolated from the production environment to reduce the risk of compromise or ransomware infection. Use edge computing platforms with Gen 3 out-of-band (OOB) management for control plane/data plane separation to improve resilience and ensure continuous remote access for troubleshooting and recovery. 

3. Analyzing patient health data generated by mobile devices in the healthcare industry

Healthcare organizations use data analysis software, including AI and machine learning, to analyze patient health data generated by insulin pumps, pacemakers, imaging devices, and other IoT medical technology. Keeping that data secure is critical for regulatory compliance, so it must be funneled through a firewall on its way to cloud-based or data center applications, increasing latency and preventing real-time response to potentially life-threatening health issues. Edge computing for healthcare moves patient monitoring and data analysis applications to the same local network (or even the same onboard chip) as the sensors generating most of the data, reducing security risks and latency. Some edge computing applications for healthcare can operate without a network connection most of the time, using built-in cellular interfaces and ATT FirstNet connections to send emergency alerts as needed without exposing any private patient data.

Best practice: Protect patient privacy by deploying healthcare edge computing solutions like Nodegrid with strong hardware roots-of-trust, Zero Trust Edge integrations, and control plane/data plane separation. Nodegrid secures management interfaces with the Trusted Platform Module 2.0 (TPM 2.0), multi-factor authentication (MFA), secure boot, built-in firewall intrusion prevention, and more.

4. Reducing latency for live streaming events and online gaming

Streaming live content requires low-latency processing for every user regardless of their geographic location, which is hard to deliver from a few large, strategically placed data centers. Edge computing decentralizes computing resources, using relatively small deployments in many different locations to bring services closer to audience members and gamers. Edge computing reduces latency for streaming sports games, concerts, and other live events, as well as online multiplayer games where real-time responses are critical to the customer experience.

Best practice: Use all-in-one, vendor-neutral devices like the Nodegrid Gate SR to combine SD-WAN, OOB management, edge security, service delivery, and more. Nodegrid services routers reduce the tech stack at each edge computing site, allowing companies to scale out as needed while minimizing hardware overhead.

5. Improving performance and business outcomes for artificial intelligence/machine learning

Artificial intelligence and machine learning applications provide enhanced data analysis capabilities for essentially any use case, but they must ingest vast amounts of data to do so. Securely transmitting and storing edge and IoT data and preparing it for ingestion in data lakes or data warehouses located in the cloud or data center takes significant time and effort, which may prevent companies from getting the most out of their AI investment. Edge computing for AI/ML eliminates transmission and storage concerns by processing data directly from the sources. Edge computing lets companies leverage their edge data for AI/ML much faster, enabling near-real-time insights, improving application performance, and providing accelerated business value from AI investments.

Best practice: Use a vendor-neutral OOB management platform like Nodegrid to remotely monitor and orchestrate edge AI/ML deployments. Nodegrid OOB ensures 24/7 remote management access to AI infrastructure even during network outages. It also supports third-party automation for mixed-vendor devices to help streamline edge operations. 

6. Enhancing remote surveillance capabilities at banks and ATMs

Constantly monitoring video surveillance feeds from banks and ATMs is very tedious for people, but machines excel at it. AI-powered video surveillance systems use advanced machine-learning algorithms to analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security teams. With edge computing, these solutions can analyze surveillance data in real-time, so they could potentially catch a crime as it’s occurring. Edge computing also keeps surveillance data on-site, reducing bandwidth costs, network latency, and the risk of interception.

Best practice: Isolate the management interfaces for all surveillance systems using a Gen 3 OOB solution like Nodegrid to keep malicious actors from hijacking the security feeds. OOB control plane/data plane separation also makes it easier to establish a secure environment for regulated financial data, simplifying PCI DSS 4.0 and DORA compliance.

7. Extending data analysis to agriculture sites with limited Internet access

The agricultural sector uses IoT technology to monitor growing conditions, equipment performance, crop yield, and much more. Many of these devices use cellular connections to transmit data to the cloud for analysis which, as we’ve already discussed ad nauseam, introduces latency, increases bandwidth costs, and creates security risks. Edge computing moves this data processing on-site to reduce delays in critical applications like livestock monitoring and irrigation control. It also allows farms to process data on a local network, reducing their reliance on cellular networks that aren’t always reliable in remote and rural areas.

Best practice: Deploy all-in-one edge gateway routers with environmental sensors, like the Nodegrid Mini SR, to monitor operating conditions where your critical infrastructure is deployed. Nodegrid’s environmental sensors alert remote teams when the temperature, humidity, or airflow falls outside of established baselines to prevent equipment failure. 

Edge computing for any use case

The potential uses for edge computing are nearly limitless. A shift toward distributed, real-time data analysis allows companies in any industry to get faster insights, reduce inefficiencies, and see more value from AI initiatives.

Simplify your edge deployment with Nodegrid

The Nodegrid line of integrated services routers delivers all-in-one edge networking, computing, security, and more. For more edge computing examples using Nodegrid, reach out to ZPE Systems today. Contact Us

Edge Computing vs Cloud Computing

by | Jun 12, 2024 | Application Hosting, Edge Computing, Micro-segmentation, Minimize Impact of Disruptions, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Secure Access Service Edge (SASE), Security Service Edge (SSE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP), Zero Trust Security

A factory floor with digital overlays showing edge computing data analysis dashboards

Both edge computing and cloud computing involve moving computational resources – such as CPUs (central processing units), GPUs (graphics processing units), RAM (random access memory), and data storage – out of the centralized, on-premises data center. As such, both represent massive shifts in enterprise network designs and how companies deploy, manage, secure, and use computing resources. Edge and cloud computing also create new opportunities for data processing, which is sorely needed as companies generate more data than ever before, thanks in no small part to an explosion in Internet of Things (IoT) and artificial intelligence (AI) adoption. This year, IoT devices alone are predicted to generate 80 zettabytes of data, much of it decentralized around the edges of the network. AI, machine learning, and other data analytics applications, meanwhile, require vast quantities of data (and highly scalable infrastructure) to provide accurate insights. This guide compares edge computing vs cloud computing to help organizations choose the right deployment model for their use case.

 Table of Contents

Defining edge computing vs cloud computing

Edge computing involves deploying computing capabilities to the network’s edges to enable on-site data processing for Internet of Things (IoT) sensors, operational technology (OT), automated infrastructure, and other edge devices and services. Edge computing deployments are highly distributed across remote sites far from the network core, such as oil & gas rigs, automated manufacturing plants, and shipping warehouses. Ideally, organizations use a centralized (usually cloud-based) orchestrator to oversee and conduct operations across the distributed edge computing architecture.

Diagram showing an example edge computing architecture controlled by a cloud-based edge orchestrator.

Reducing the number of network hops between edge devices and the applications that process and use edge data enables real-time data processing, reduces MPLS bandwidth costs, improves performance, and keeps private data within the security micro-perimeter. Cloud computing involves using remote computing resources over the Internet to run applications, process and store data, and more. Cloud service providers manage the physical infrastructure and allow companies to easily scale their virtual computing resources with the click of a button, significantly reducing operational costs and complexity over on-premises and edge computing deployments.

Examples of edge computing vs cloud computing

Edge computing works best for workloads requiring real-time data processing using fairly lightweight applications, especially in locations with inconsistent or unreliable Internet access or where privacy/compliance is a major concern. Example edge computing use cases include:

Cloud computing is well-suited to workloads requiring extensive computational resources that can scale on-demand, but that aren’t time-sensitive. Example use cases include:

The advantages of edge computing over cloud computing

Using cloud-based applications to process edge device data involves transmitting that data from the network’s edges to the cloud provider’s data center, and vice versa. Transmitting data over the open Internet is too risky, so most organizations route the traffic through a security appliance such as a firewall to encrypt and protect the data. Often these security solutions are off-site, in the company’s central data center, or, best-case scenario, a SASE point-of-presence (PoP), adding more network hops between edge devices and the cloud applications that service them.  This process increases bandwidth usage and introduces latency, preventing real-time data processing and negatively affecting performance, which is one of the main reasons why organizations are repatriating workloads from the cloud to on-prem.

Edge computing moves data processing resources closer to the source, eliminating the need to transmit this data over the Internet. This improves performance by reducing (or even removing) network hops and preventing network bottlenecks at the centralized firewall. Edge computing also lets companies use their valuable edge data in real time, enabling faster insights and greater operational efficiencies.

Edge computing mitigates the risk involved in storing and processing sensitive or highly regulated data in a third-party computing environment, giving companies complete control over their data infrastructure. It can also help reduce bandwidth costs by eliminating the need to route edge data through VPNs or MPLS links to apply security controls.

Edge computing advantages:

  • Improves network and application performance
  • Enables real-time data processing and insights
  • Simplifies security and compliance
  • Reduces MPLS bandwidth costs

The disadvantages of edge computing compared to cloud computing

Cloud computing resources are highly scalable, allowing organizations to meet rapidly changing requirements without the hassle of purchasing, installing, and maintaining additional hardware and software licenses. Edge computing still involves physical, on-premises infrastructure, making it far less scalable than the cloud. However, it’s possible to improve edge agility and flexibility by using vendor-neutral platforms to run and manage edge resources. An open platform like Nodegrid allows teams to run multiple edge computing applications from different vendors on the same box, swap out services as business needs evolve, and deploy automation to streamline multi-vendor edge device provisioning from a single orchestrator. A diagram showing how the Nodegrid Mini SR combines edge computing and networking capabilities on a small, affordable, flexible platform.

Diagram showing how the Nodegrid Mini SR combines edge computing and networking capabilities on a small, affordable, flexible platform.

Organizations often deploy edge computing in less-than-ideal operating environments, such as closets and other cramped spaces that lack the strict HVAC controls that maintain temperature and humidity in cloud data centers. These environments also typically lack the physical security controls that prevent unauthorized individuals from tampering with equipment, such as guarded entryways, security cameras, and biometric locks. The best way to mitigate this disadvantage is with an environmental monitoring system that uses sensors to detect temperature and humidity changes that could cause equipment failures as well as proximity alarms to notify administrators when someone gets too close. It’s also advisable to use hermetically sealed edge computing devices capable of operating in extreme temperatures and with built-in security features making them tamper-proof.

Cloud computing is often more resilient than edge computing because cloud service providers must maintain a certain level of continuous uptime to meet service level agreements (SLAs). Edge computing operations could be disrupted by network equipment failures, ISP outages, ransomware attacks, and other adverse events, so it’s essential to implement resilience measures that keep services running (if in a degraded state) and allow remote teams to fix problems without having to be on site. Edge resilience measures include Gen 3 out-of-band management, control plane/data plane separation (also known as isolated management infrastructure or IMI), and isolated recovery environments (IRE).

Edge computing disadvantages:

  • Less scalable than cloud infrastructure
  • Lack of environmental and security controls
  • Requires additional resilience measures

Edge-native applications vs cloud-native applications

Edge-native applications and cloud-native applications are similar in that they use containers and microservices architectures, as well as CI/CD (continuous integration/continuous delivery) and other DevOps principles.

Cloud-native applications leverage centralized, scalable resources to perform deep analysis of long-lived data in long-term hot storage environments. Edge-native applications are built to leverage limited resources distributed around the network’s edges to perform real-time analysis of ephemeral data that’s constantly moving. Typically, edge-native applications are highly contextualized for a specific use case, whereas cloud-native applications offer broader, standardized capabilities. Another defining characteristic of edge-native applications is the ability to operate independently when needed while still integrating seamlessly with the cloud, upstream resources, remote management, and centralized orchestration.

Choosing edge computing vs cloud computing

Both edge computing and cloud computing have unique advantages and disadvantages that make them well-suited for different workloads and use cases. Factors like increasing data privacy regulations, newsworthy cloud provider outages, greater reliance on human-free IoT and OT deployments, and an overall trend toward decentralizing business operations are pushing organizations to adopt edge computing. However, most companies still rely heavily on cloud resources and will continue to do so, making it crucial to ensure seamless interoperability between the edge and the cloud.

The best way to ensure integration is by using vendor-neutral platforms. For example, Nodegrid integrated services routers like the Gate SR provide multi-vendor out-of-band serial console management for edge infrastructure and devices, using an embedded Jetson Nano card to support edge computing and AI workloads. The ZPE Cloud management platform unifies orchestration for the entire Nodegrid-connected architecture, delivering 360-degree control over complex and highly distributed networks. Plus, Nodegrid easily integrates – or even directly hosts – other vendors’ solutions for edge data processing, IT automation, SASE, and more, making edge operations more cost-effective. Nodegrid also provides the complete control plane/data plane separation needed to ensure edge resilience.

Get edge efficiency and resilience with Nodegrid

The Nodegrid platform from ZPE Systems helps companies across all industries streamline their edge operations with resilient, vendor-neutral, Gen 3 out-of-band management. Request a free Nodegrid demo to learn more. REQUEST A DEMO

Edge Computing Architecture Guide

by | Jun 6, 2024 | Application Hosting, Edge Computing, Micro-segmentation, Minimize Impact of Disruptions, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Secure Access Service Edge (SASE), Security Service Edge (SSE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP), Zero Trust Security

Edge-computing-architecture-concept-icons-arranged-around-the-word-edge-computing
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?

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.

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

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.

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

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.

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