Using vendor-neutral platforms to host, connect, and secure edge applications and workloads is the best practice for ensuring the scalability and flexibility of financial edge architectures. Moving away from dedicated device stacks and taking a “platformization” approach allows financial institutions to easily deploy, update, and swap out applications and capabilities on demand. Vendor-neutral platforms help reduce hardware overhead costs to deploy new branches and allow banks to explore different edge software capabilities without costly hardware upgrades.

Additionally, using a centralized, cloud-based edge management and orchestration (EMO) platform is the best practice for ensuring remote teams have holistic oversight of the distributed edge computing architecture. This platform should be vendor-agnostic to ensure complete coverage over mixed and legacy architectures, and it should use out-of-band (OOB) management to provide continuous remote access to edge infrastructure even during a major service outage.

How Nodegrid streamlines edge computing for the banking industry

Nodegrid is a vendor-neutral edge networking platform that consolidates an entire edge tech stack into a single, cost-effective device. Nodegrid has a Linux-based OS that supports third-party VMs and Docker containers, allowing banks to run edge computing workloads, data analytics software, automation, security, and more. 

The Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for artificial intelligence workloads. This allows banks to run AI surveillance software, ML-powered recommendation engines, and AIOps at the edge alongside networking and infrastructure workloads rather than purchasing expensive, dedicated GPU resources. Plus, Nodegrid’s Gen 3 OOB management ensures continuous remote access and IMI for improved branch resilience.

Get Nodegrid for your edge computing use cases in banking

Nodegrid’s flexible, vendor-neutral platform adapts to any use case and deployment environment. Watch a demo to see Nodegrid’s financial network solutions in action.

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The goal of AI orchestration is to provide a single, unified platform for teams to oversee and manage AI-related workflows across the entire organization. This post describes the ideal AI orchestration solution and the technologies that make it work, helping companies use artificial intelligence more efficiently.

AI challenges to overcome

The challenges an organization must overcome to use AI more cost-effectively and see faster returns can be broken down into three categories:

1. Overseeing AI-led workflows to ensure models are behaving as expected and providing accurate results, when these workflows are spread across the enterprise in different geographic locations and vendor-specific applications.
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2. Efficiently provisioning, maintaining, and scaling the vast infrastructure and computational resources required to run intensive AI workflows at remote data centers and edge computing sites.
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3. Maintaining 24/7 availability and performance of remote AI workflows and infrastructure during security breaches, equipment failures, network outages, and natural disasters.

These challenges have a few common causes. One is that artificial intelligence and the underlying infrastructure that supports it are highly complex, making it difficult for human engineers to keep up. Two is that many IT environments are highly fragmented due to closed vendor solutions that integrate poorly and require administrators to manage too many disparate systems, allowing coverage gaps to form. Three is that many AI-related workloads occur off-site at data centers and edge computing sites, so it’s harder for IT teams to repair and recover AI systems that go down due to a networking outage, equipment failure, or other disruptive event.

How AI orchestration streamlines AI/ML in an enterprise environment

The ideal AI orchestration platform solves these problems by automating repetitive and data-heavy tasks, unifying workflows with a vendor-neutral platform, and using out-of-band (OOB) serial console management to provide continuous remote access even during major outages.

Automation

Automation is crucial for teams to keep up with the pace and scale of artificial intelligence. Organizations use automation to provision and install AI data center infrastructure, manage storage for AI training and inference data, monitor inputs and outputs for toxicity, perform root-cause analyses when systems fail, and much more. However, tracking and troubleshooting so many automated workflows can get very complicated, creating more work for administrators rather than making them more productive. An AI orchestration platform should provide a centralized interface for teams to deploy and oversee automated workflows across applications, infrastructure, and business sites.

Unification

The best way to improve AI operational efficiency is to integrate all of the complicated monitoring, management, automation, security, and remediation workflows. This can be accomplished by choosing solutions and vendors that interoperate or, even better, are completely vendor-agnostic (a.k.a., vendor-neutral). For example, using open, common platforms to run AI workloads, manage AI infrastructure, and host AI-related security software can help bring everything together where administrators have easy access. An AI orchestration platform should be vendor-neutral to facilitate workload unification and streamline integrations.

Resilience

AI models, workloads, and infrastructure are highly complex and interconnected, so an issue with one component could compromise interdependencies in ways that are difficult to predict and troubleshoot. AI systems are also attractive targets for cybercriminals due to their vast, valuable data sets and because of how difficult they are to secure, with HiddenLayer’s 2024 AI Threat Landscape Report finding that 77% of businesses have experienced AI-related breaches in the last year. An AI orchestration platform should help improve resilience, or the ability to continue operating during adverse events like tech failures, breaches, and natural disasters.

Gen 3 out-of-band management technology is a crucial component of AI and network resilience. A vendor-neutral OOB solution like the Nodegrid Serial Console Plus (NSCP) uses alternative network connections to provide continuous management access to remote data center, branch, and edge infrastructure even when the ISP, WAN, or LAN connection goes down. This gives administrators a lifeline to troubleshoot and recover AI infrastructure without costly and time-consuming site visits. The NSCP allows teams to remotely monitor power consumption and cooling for AI infrastructure. It also provides 5G/4G LTE cellular failover so organizations can continue delivering critical services while the production network is repaired.

Gen 3 OOB also helps organizations implement isolated management infrastructure (IMI), a.k.a, control plane/data plane separation. This is a cybersecurity best practice recommended by the CISA as well as regulations like PCI DSS 4.0, DORA, NIS2, and the CER Directive. IMI prevents malicious actors from being able to laterally move from a compromised production system to the management interfaces used to control AI systems and other infrastructure. It also provides a safe recovery environment where teams can rebuild and restore systems during a ransomware attack or other breach without risking reinfection.

Getting the most out of your AI investment

An AI orchestration platform should streamline workflows with automation, provide a unified platform to oversee and control AI-related applications and systems for maximum efficiency and coverage, and use Gen 3 OOB to improve resilience and minimize disruptions. Reducing management complexity, risk, and repair costs can help companies see greater productivity and financial returns from their AI investments.

The vendor-neutral Nodegrid platform from ZPE Systems provides highly scalable Gen 3 OOB management for up to 96 devices with a single, 1RU serial console. The open Nodegrid OS also supports VMs and Docker containers for third-party applications, so you can run AI, automation, security, and management workflows all from the same device for ultimate operational efficiency.

Streamline AI orchestration with Nodegrid

Contact ZPE Systems today to learn more about using a Nodegrid serial console as the foundation for your AI orchestration platform. Contact Us

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for edge computing in the telecommunications industry.

4 Edge computing use cases in telecom

1. Enhancing the customer experience with real-time analytics

Each customer interaction, from sales calls to repair requests and service complaints, is a chance to collect and leverage data to improve the experience in the future. Transferring that data from customer sites, regional branches, and customer service centers to a centralized data analysis application takes time, creates network latency, and can make it more difficult to get localized and context-specific insights. Edge computing allows telecom companies to analyze valuable customer experience data, such as network speed, uptime (or downtime) count, and number of support contacts in real-time, providing better opportunities to identify and correct issues before they go on to affect future interactions.

2. Streamlining remote infrastructure management and recovery with AIOps

AIOps helps telecom companies manage complex, distributed network infrastructure more efficiently. AIOps (artificial intelligence for IT operations) uses advanced machine learning algorithms to analyze infrastructure monitoring data and provide maintenance recommendations, automated incident management, and simple issue remediation. Deploying AIOps on edge computing devices at each telecom site enables real-time analysis, detection, and response, helping to reduce the duration of service disruptions. For example, AIOps can perform automated root-cause analysis (RCA) to help identify the source of a regional outage before technicians arrive on-site, allowing them to dive right into the repair. Edge AIOps solutions can also continue functioning even if the site is cut off from the WAN or Internet, potentially self-healing downed networks without the need to deploy repair techs on-site.

3. Preventing environmental conditions from damaging remote equipment

Telecommunications equipment is often deployed in less-than-ideal operating conditions, such as unventilated closets and remote cell site shelters. Heat, humidity, and air particulates can shorten the lifespan of critical equipment or cause expensive service failures, which is why it’s recommended to use environmental monitoring sensors to detect and alert remote technicians to problems. Edge computing applications can analyze environmental monitoring data in real-time and send alerts to nearby personnel much faster than cloud- or data center-based solutions, ensuring major fluctuations are corrected before they damage critical equipment.

4. Improving operational efficiency with network virtualization and consolidation

Another way to reduce management complexity – as well as overhead and operating expenses – is through virtualization and consolidation. Network functions virtualization (NFV) virtualizes networking equipment like load balancers, firewalls, routers, and WAN gateways, turning them into software that can be deployed anywhere – including edge computing devices. This significantly reduces the physical tech stack at each site, consolidating once-complicated network infrastructure into, in some cases, a single device. For example, the Nodegrid Gate SR provides a vendor-neutral edge computing platform that supports third-party NFVs while also including critical edge networking functionality like out-of-band (OOB) serial console management and 5G/4G cellular failover.

Edge computing in telecom: Benefits and best practices

Edge computing can help telecommunications companies:

• Get actionable insights that can be leveraged in real-time to improve network performance, service reliability, and the support experience.
• Reduce network latency by processing more data at each site instead of transmitting it to the cloud or data center for analysis.
• Lower CAPEX and OPEX at each site by consolidating the tech stack and automating management workflows with AIOps.
• Prevent downtime with real-time analysis of environmental and equipment monitoring data to catch problems before they escalate.
• Accelerate recovery with real-time, AIOps root-cause analysis and simple incident remediation that continues functioning even if the site is cut off from the WAN or Internet.

Management infrastructure isolation, which is recommended by CISA and required by regulations like DORA, is the best practice for improving edge resilience and ensuring a speedy recovery from failures and breaches. Isolated management infrastructure (IMI) prevents compromised accounts, ransomware, and other threats from moving laterally from production resources to the interfaces used to control critical network infrastructure.

Vendor-neutral platforms help reduce hardware overhead costs to deploy new edge sites, make it easy to spin-up new NFVs to meet increased demand, and allow telecom organizations to explore different edge software capabilities without costly hardware upgrades. For example, the Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for AI workloads, so companies can run innovative artificial intelligence at the edge alongside networking and infrastructure management workloads rather than purchasing expensive, dedicated GPU resources.

Streamlined, cost-effective edge computing with Nodegrid

Nodegrid’s flexible, vendor-neutral platform adapts to all edge computing use cases in telecom. Watch a demo to see Nodegrid’s telecom solutions in action.

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Edge computing mitigates many of these challenges by decentralizing cloud and data center resources and distributing them at the network’s “edges,” where most retail operations take place. Running applications and processing data at the edge enables real-time analysis and insights and ensures that systems remain operational even if Internet access is disrupted by an ISP outage or natural disaster. This blog describes five potential edge computing use cases in retail and provides more information about the benefits of edge computing for the retail industry.

5 Edge computing use cases in retail

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1. Security video analysis

Security cameras are crucial to loss prevention, but constantly monitoring video surveillance feeds is tedious and difficult for even the most experienced personnel. AI-powered video surveillance systems use machine learning to analyze video feeds and detect suspicious activity with greater vigilance and accuracy. Edge computing enhances AI surveillance by allowing solutions to analyze video feeds in real-time, potentially catching shoplifters in the act and preventing inventory shrinkage.

2. Localized, real-time insights

Retailers have a brief window to meet a customer’s needs before they get frustrated and look elsewhere, especially in a brick-and-mortar store. A retail store can use an edge computing application to learn about customer behavior and purchasing activity in real-time. For example, they can use this information to rotate the products featured on aisle endcaps to meet changing demand, or staff additional personnel in high-traffic departments at certain times of day. Stores can also place QR codes on shelves that customers scan if a product is out of stock, immediately alerting a nearby representative to provide assistance.

3. Enhanced inventory management

Effective inventory management is challenging even for the most experienced retail managers, but ordering too much or too little product can significantly affect sales. Edge computing applications can improve inventory efficiency by making ordering recommendations based on observed purchasing patterns combined with real-time stocking updates as products are purchased or returned. Retailers can use this information to reduce carrying costs for unsold merchandise while preventing out-of-stocks, improving overall profit margins.
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4. Building management

Using IoT devices to monitor and control building functions such as HVAC, lighting, doors, power, and security can help retail organizations reduce the need for on-site facilities personnel, and make more efficient use of their time. Data analysis software helps automatically optimize these systems for efficiency while ensuring a comfortable customer experience. Running this software at the edge allows automated processes to respond to changing conditions in real-time, for example, lowering the A/C temperature or routing more power to refrigerated cases during a heatwave.

5. Warehouse automation

The retail industry uses warehouse automation systems to improve the speed and efficiency at which goods are delivered to stores or directly to users. These systems include automated storage and retrieval systems, robotic pickers and transporters, and automated sortation systems. Companies can use edge computing applications to monitor, control, and maintain warehouse automation systems with minimal latency. These applications also remain operational even if the site loses internet access, improving resilience.

The benefits of edge computing for retail

The benefits of edge computing in a retail setting include:
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For example, the Nodegrid Gate SR integrated branch services router delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and Docker containers for third-party edge computing applications, security solutions, and more. The Gate SR is also available with an Nvidia Jetson Nano card that’s optimized for AI workloads to help retail organizations reduce the hardware overhead costs of deploying artificial intelligence at the edge.

Consolidated edge computing with Nodegrid

Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in retail. Watch a demo to see Nodegrid’s retail network solutions in action.

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Edge computing deploys data analytics applications and computing resources around the edges of the network, where much of the most valuable data is created. This significantly reduces latency and mitigates many security and compliance risks. In a healthcare setting, edge computing enables real-time medical insights and interventions while keeping HIPAA-regulated data within the local security perimeter. This blog describes six potential edge computing use cases in healthcare that take advantage of the speed and security of an edge computing architecture.

6 Edge computing use cases in healthcare

Edge computing use cases for EMS

Mobile emergency medical services (EMS) teams need to make split-second decisions regarding patient health without the benefit of a doctorate and, often, with spotty Internet connections preventing access to online drug interaction guides and other tools. Installing edge computing resources on cellular edge routers gives EMS units real-time health analysis capabilities as well as a reliable connection for research and communications. Potential use cases include:
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Edge computing use cases in hospitals & clinics

Hospitals and clinics use IoT devices to monitor vitals, dispense medications, perform diagnostic tests, and much more. Sending all this data to the cloud or data center takes time, delaying test results or preventing early intervention in a health crisis, especially in rural locations with slow or spotty Internet access. Deploying applications and computing resources on the same local network enables faster analysis and real-time alerts. Potential use cases include:
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Edge computing use cases for wearable medical devices

Wearable medical devices give patients and their caregivers greater control over health outcomes. With edge computing, health data analysis software can run directly on the wearable device, providing real-time results even without an Internet connection. Potential use cases include:
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The benefits of edge computing for healthcare

Using edge computing in a healthcare setting as described in the use cases above can help organizations:

• Improve patient care in remote settings, where a lack of infrastructure limits the ability to use cloud-based technology solutions.
• Process and analyze patient health data faster and more reliably, leading to earlier interventions.
• Increase efficiency by assisting understaffed medical teams with diagnostics, patient monitoring, and communications.
• Mitigate security and compliance risks by keeping health data within the local security perimeter.

Edge computing can also help healthcare organizations lower their operational costs at the edge by reducing bandwidth utilization and cloud data storage expenses. Another way to reduce costs is by using consolidated, vendor-neutral solutions to host, connect, and secure edge applications and workloads.

For example, the Nodegrid Gate SR is an integrated branch services router that delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. Nodegrid’s open, Linux-based OS supports VMs and Docker containers for third-party edge applications, security solutions, and more. Plus, an onboard Nvidia Jetson Nano card is optimized for AI workloads at the edge, significantly reducing the hardware overhead costs of using artificial intelligence at remote healthcare sites. Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in healthcare, future-proofing your edge architecture.

Streamline your edge deployment with Nodegrid

The vendor-neutral Nodegrid platform consolidates an entire edge technology stack into a unified, streamlined solution. Watch a demo to see Nodegrid’s healthcare network solutions in action.

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Edge computing delivers data processing and analysis capabilities to the network’s “edge,” at remote sites like branch offices, warehouses, retail stores, and manufacturing plants. It involves deploying computing resources and lightweight applications very near the devices that generate data, reducing the distance and number of network hops between them. In doing so, edge computing reduces latency and bandwidth costs while mitigating risk, enhancing edge resilience, and enabling real-time insights. This blog discusses the five biggest benefits of edge computing, providing examples and additional resources for companies beginning their edge journey.
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5 benefits of edge computing​

1. Reduces latency

Edge computing leverages data on the same local network as the devices that generate it, cutting down on edge data transmissions over the WAN or Internet. Reducing the number of network hops between devices and applications significantly decreases latency, improving the speed and performance of business intelligence apps, AIOps, equipment health analytics, and other solutions that use edge data.

Some edge applications run on the devices themselves, completely eliminating network hops and facilitating real-time, lag-free analysis. For example, an AI-powered surveillance application installed on an IoT security camera at a walk-up ATM can analyze video feeds in real-time and alert security personnel to suspicious activity as it occurs.​

2. Mitigates risk

Edge computing mitigates security and compliance risks by distributing an organization’s sensitive data and reducing off-site transmission. Large, centralized data stores in the cloud or data center are prime targets for cybercriminals because the sheer volume of data involved increases the chances of finding something valuable. Decentralizing data in much smaller edge storage solutions makes it harder for hackers to find the most sensitive information and also limits how much data they can access at one time.

Keeping data at the edge also reduces the chances of interception in transit to cloud or data center storage. Plus, unlike in the cloud, an organization maintains complete control over who and what has access to sensitive data, aiding in compliance with regulations like the GDPR and PCI DSS 4.0.
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3. Lowers bandwidth costs

Many organizations use MPLS (multi-protocol label switching) links to securely connect edge sites to the enterprise network. MPLS bandwidth is much more expensive than regular Internet lines, which makes transmitting edge data to centralized data processing applications extremely costly. Plus, it can take months to provision MPLS at a new site, delaying launches and driving up overhead expenses.

Edge computing significantly reduces MPLS bandwidth utilization by running data-hungry applications on the local network, reserving the WAN for other essential traffic. Combining edge computing with SD-WAN (software-defined wide area networking) and SASE (secure access service edge) technologies can markedly decrease the reliance on MPLS links, allowing organizations to accelerate branch openings and see faster edge ROIs.
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4. Enhances edge resilience

Since edge computing applications run on the same LAN as the devices generating data, they can continue to function even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event. This also allows uninterrupted edge operations in locations with inconsistent (or no) Internet coverage, like offshore oil rigs, agricultural sites, and health clinics in isolated rural communities. Edge computing ensures that organizations don’t miss any vital health or safety alerts and facilitates technological innovation using AI and other data analytics tools in challenging environments..
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5. Enables real-time insights

Sending data from the edge to a cloud or on-premises data lake for processing, transformation, and ingestion by analytics or AI/ML tools takes time, preventing companies from acting on insights at the moment when they’re most useful. Edge computing applications start using data as soon as it’s generated, so organizations can extract value from it right away. For example, a retail store can use edge computing to gain actionable insights on purchasing activity and customer behavior in real-time, so they can move in-demand products to aisle endcaps or staff extra cashiers as needed.
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Simplify your edge computing deployment with Nodegrid

The best way to achieve the benefits of edge computing described above without increasing management complexity or hardware overhead is to use consolidated, vendor-neutral solutions to host, connect, and secure edge workloads. For example, the Nodegrid Gate SR from ZPE Systems delivers an entire stack of edge networking and infrastructure management technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and containers for third-party applications, with an Nvidia Jetson Nano card capable of running AI workloads alongside non-AI data analytics for ultimate efficiency.

Improve your edge computing deployment with Nodegrid

Nodegrid consolidates edge computing deployments to improve operational efficiency without sacrificing performance or functionality. Schedule a free demo to see Nodegrid in action.

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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

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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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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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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. By 2025, 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.

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:

• Orchestrating operational technology (OT) in remote manufacturing plants
• Monitoring safety and production data from sensors inside gas lines
• Analyzing patient data from wearable health monitors to provide real-time emergency alerts

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:

• Running large artificial intelligence and machine learning solutions
• Centralizing collaboration tools like source code version control
• Orchestrating distributed edge operations from a single platform

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.

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.

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.

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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

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:

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:

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.

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