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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AI data center infrastructure components

Computing

Generative AI and other artificial intelligence technologies require significant processing power. AI workloads typically run on graphics processing units (GPUs), which are made up of many smaller cores that perform simple, repetitive computing tasks in parallel. GPUs can be clustered together to process data for AI much faster than CPUs.

Storage

AI requires vast amounts of data for training and inference. On-premises AI data centers typically use object storage systems with solid-state disks (SSDs) composed of multiple sections of flash memory (a.k.a., flash storage). Storage solutions for AI workloads must be modular so additional capacity can be added as data needs grow, through either physical or logical (networking) connections between devices.

Networking

AI workloads are often distributed across multiple computing and storage nodes within the same data center. To prevent packet loss or delays from affecting the accuracy or performance of AI models, nodes must be connected with high-speed, low-latency networking. Additionally, high-throughput WAN connections are needed to accommodate all the data flowing in from end-users, business sites, cloud apps, IoT devices, and other sources across the enterprise.

Power

AI infrastructure uses significantly more power than traditional data center infrastructure, with a rack of three or four AI servers consuming as much energy as 30 to 40 standard servers. To prevent issues, these power demands must be accounted for in the layout design for new AI data center deployments and, if necessary, discussed with the colocation provider to ensure enough power is available.

Management

Data center infrastructure, especially at the scale required for AI, is typically managed with a jump box, terminal server, or serial console that allows admins to control multiple devices at once. The best practice is to use an out-of-band (OOB) management device that separates the control plane from the data plane using alternative network interfaces. An OOB console server provides several important functions:

1. It provides an alternative path to data center infrastructure that isn’t reliant on the production ISP, WAN, or LAN, ensuring remote administrators have continuous access to troubleshoot and recover systems faster, without an on-site visit.
2. It isolates management interfaces from the production network, preventing malware or compromised accounts from jumping over from an infected system and hijacking critical data center infrastructure.
3. It helps create an isolated recovery environment where teams can clean and rebuild systems during a ransomware attack or other breach without risking reinfection.

An OOB serial console helps minimize disruptions to AI infrastructure. For example, teams can use OOB to remotely control PDU outlets to power cycle a hung server. Or, if a networking device failure brings down the LAN, teams can use a 5G cellular OOB connection to troubleshoot and fix the problem. Out-of-band management reduces the need for costly, time-consuming site visits, which significantly improves the resilience of AI infrastructure.

AI data center challenges

Artificial intelligence workloads, and the data center infrastructure needed to support them, are highly complex. Many IT teams struggle to efficiently provision, maintain, and repair AI data center infrastructure at the scale and speed required, especially when workflows are fragmented across legacy and multi-vendor solutions that may not integrate. The best way to ensure data center teams can keep up with the demands of artificial intelligence is with a unified AI orchestration platform. Such a platform should include:

• Automation for repetitive provisioning and troubleshooting tasks
• Unification of all AI-related workflows with a single, vendor-neutral platform
• Resilience with cellular failover and Gen 3 out-of-band management.

To learn more, read AI Orchestration: Solving Challenges to Improve AI Value

Improving operational efficiency with a vendor-neutral platform

Nodegrid is a Gen 3 out-of-band management solution that provides the perfect unification platform for AI data center orchestration. The vendor-neutral Nodegrid platform can integrate with or directly run third-party software, unifying all your networking, management, automation, security, and recovery workflows. A single, 1RU Nodegrid Serial Console Plus (NSCP) can manage up to 96 data center devices, and even extend automation to legacy and mixed-vendor solutions that wouldn’t otherwise support it. Nodegrid Serial Consoles enable the fast and cost-efficient infrastructure scaling required to support GenAI and other artificial intelligence technologies.

Make Nodegrid your AI data center orchestration platform

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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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The Cisco ISR 4431 is an enterprise branch services router from Cisco’s Integrated Services Router product line. The ISR 4431 integrates with the Cisco DNA infrastructure management platform and the Catalyst SD-WAN (software-defined wide area networking) solution. Its modular design also makes the ISR 4431 extensible with Cisco’s Network Interface Modules (NIMs) to add storage, Ethernet switching, out-of-band (OOB) console server management, and other capabilities.

Cisco announced end-of-sale and end-of-life (EOL) dates for select ISR 4400-series models, including the ISR 4431. Its Cisco-recommended replacement option is the Catalyst C8300, which offers some improvements over the ISR but still suffers from some management, automation, and scaling limitations. However, there are other options on the market that fill these gaps with secure, vendor-neutral, all-in-one branch networking solutions. This guide compares Cisco ISR 4431 EOL replacement options and discusses the advanced features and capabilities offered by Cisco alternatives.

Click here for a list of ISR 4431 EOL products and replacement SKUs.
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Upcoming Cisco ISR 4431 EOL dates

• November 6, 2024 – End of routine failure analysis, end of new service attachment
• August 31, 2025 – End of software maintenance releases and bug fixes
• February 5, 2028 – End of service contract renewal
• November 30, 2028 – Last date of support

Cisco ISR 4431 EOL replacement options

The Cisco Catalyst C8300

The Cisco ISR 4431 suffers from numerous limitations, such as its large physical size and closed ecosystem. Cisco’s recommended replacement option, the Catalyst C8300, has the same problems.

Both devices are 2RU, making them too large to easily install in cramped branches and edge computing sites that may not have a dedicated IT space. Both the ISR 4431 and the Catalyst C8300 are closed platforms, only supporting integrations with Cisco’s third-party partners like ThousandEyes. This prevents teams from utilizing all the security, automation, and monitoring solutions they’re most familiar with (or that work best for their specific use case), increasing the difficulty and complexity of branch network operations. Cisco’s OOB management modules and DNA software are also mostly limited to controlling other Cisco devices, leaving administrators with critical coverage gaps or multiple management solutions to deal with. Overall, these limitations reduce the efficiency, resilience, and scalability of branch network operations.

The Nodegrid Net SR (NSR)

The Nodegrid platform from ZPE Systems addresses many of Cisco’s limitations with vendor-neutral branch services routers (SRs). The Nodegrid Net Services Router (NSR) is a 1RU replacement for Cisco ISR 4431 EOL devices and features advanced branch networking capabilities.

The NSR provides branch gateway routing and switching, vendor-neutral VNF (virtual network function) hosting, and out-of-band management in a single, 1RU device. The NSR’s expansion modules add capabilities like PoE+, cellular/Wi-Fi, edge compute, and additional serial console management ports.

Nodegrid solutions are vendor-neutral, supporting Guest OS and Docker containers for third-party software. Teams can use their favorite tools for monitoring, automation, and security, and even extend these capabilities to legacy and mixed-vendor infrastructure. Organizations can use Nodegrid to create a custom-tailored, all-in-one branch networking solution with all the apps and services needed to deploy, manage, troubleshoot, and recover branch operations. Plus, Nodegrid creates an isolated management plane where teams can recover from ransomware, deploy resource-intensive automated workflows, and ensure 24/7 branch operations, improving resilience and supporting efficient scaling.

Ready to replace your Cisco ISR 4431 EOL products?

The Nodegrid platform delivers vendor-neutral branch network management for improved efficiency, resilience, and scalability. See our Cisco ISR 4431 EOL replacement SKUs below or contact ZPE Systems for help choosing the right Nodegrid solution for your business.

Explore our full products and services package to replace your Cisco ISR 4431

We know that replacing EOL devices takes a lot of effort. That’s why ZPE now offers a complete package of budget-friendly products and engineering services. Visit our page to see how we make it easy to replace discontinued devices like the Cisco ISR 4431.

Cisco ISR 4431 replacement SKUs

The current edge computing landscape comprises solutions focused on individual use cases,  lacking interoperability and central orchestration. The future of edge computing, as described by leading analysts at Gartner, depends on unifying the edge computing ecosystem with comprehensive strategies and centralized, vendor-neutral management and orchestration. This future relies on edge-native applications that integrate seamlessly with upstream resources, remote management, and orchestration while still being able to operate independently.

Where is edge computing now?

Many organizations already use edge computing technology to solve individual problems or handle specific workloads. For example, a manufacturing department may deploy an edge computing application to analyze log data and provide predictive maintenance recommendations for a single type of machine or assembly line. A single company may have a dozen or more disjointed edge computing solutions in use throughout the network, creating visibility and management headaches for IT teams. This piecemeal approach to edge computing results in what Gartner calls “edge sprawl”: many disparate solutions deployed without centralized control, security, or visibility. Edge sprawl increases management complexity and risk while decreasing operational efficiency, creating significant roadblocks for digital transformation initiatives.

Additionally, many organizations misunderstand edge computing by thinking it’s just about moving computing resources as close to the edge as possible to collect data. In reality, the true potential of the edge involves using edge data in real-time, gaining “cloud-in-a-box” capability that works in concert with the network’s upstream resources.

Anticipating the future of edge computing

At Gartner’s 2023 IT Infrastructure Operations & Cloud Strategies Conference, edge technology experts predicted that, by 2025, enterprises will create and process more than 50% of their data outside the centralized data center or cloud. Surging edge data volume will accelerate the challenges caused by a lack of strategy or orchestration.

• Enabling extensibility by utilizing vendor-neutral platforms that allow for expansion and integration, which supports growth and agility at the edge.
• Looking for opportunities to deploy artificial intelligence, data analytics, and machine learning alongside edge computing units, for example, with system-on-chip technology or all-in-one edge networking and computing devices.
• Anticipating data storage and governance challenges at the edge by defining clear policies and deploying AI/ML data management solutions that dynamically determine data value.
• Reducing knowledge debt by utilizing vendor-neutral platforms that support familiar container and cluster management technologies (like Docker and Kubernetes).
• Securing the edge with a multi-layered defense, including hardware security, frequent patches, zero-trust policies, strong authentication, network micro-segmentation, and comprehensive security monitoring.
• Centralizing edge management and orchestration (EMO) with a vendor-neutral platform that unifies control, supports environmental monitoring, and uses out-of-band (OOB) management while interoperating with automated edge management workflows (such as zero-touch provisioning and infrastructure configuration management).

Bittman’s recommended edge computing strategy uses the central EMO as a hub for all the technologies, processes, and workflows involved in operating and supporting the edge. This strategy will prepare companies for the future of edge computing and support efficient, agile growth and innovation.

Enter the future of edge computing with Nodegrid

Nodegrid is a vendor-neutral edge management and orchestration platform from ZPE Systems. Nodegrid easily interoperates with your choice of edge solutions and can directly run third-party AI, ML, data analytics, and data governance applications to help you extract more value from your edge data. The open, Linux-based Nodegrid OS can also host Docker containers and edge-native applications to reduce hardware overhead and knowledge debt.

Nodegrid devices protect your edge management interfaces with hardware security features like TPM and geofencing, support for strong authentication like 2FA, and integrations with leading zero-trust providers like Okta and PING. The Nodegrid OS and ZPE Cloud are Synopsys-validated to address security at every stage of the SDLC. Plus, you can run third-party security solutions for SASE, next-generation firewalls, and more.

Nodegrid edge networking solutions use out-of-band technology to give teams 24/7 remote visibility, management, and troubleshooting access to edge deployments. It freely interoperates with third-party solutions for infrastructure automation, monitoring, and recovery to support network resilience and operational efficiency. Nodegrid is like a cloud-in-a-box solution, incorporating edge computing and the full networking stack. Nodegrid’s edge management and orchestration platform provides single-pane-of-glass visibility, control, and resilience while supporting future edge growth.

Use Nodegrid for your Gartner-approved edge computing strategy

The Nodegrid EMO platform helps you anticipate the future of edge computing with vendor-neutral, single-pane-of-glass visibility and control. Watch a free Nodegrid demo to learn more.

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Providing low-level remote access and automation saves hundreds of hours per month for the university’s small IT team

One of the largest universities in the United States fosters academics and research for nearly 40,000 students, staff, and researchers. The university sits among the top 10 schools for engineering, and heavily integrates technology into all disciplines, including engineering, computer sciences, and agricultural studies.

The university received a grant to expand, update, and connect their network of campuses, while enhancing infrastructure and mobility, resiliency, and campus amenities.  But having more than 200 on-campus buildings presents a challenge. The campus is home to academic facilities as well as a hospital, airport, 60,000-seat sports stadium, and dozens of leased spaces for local businesses. This makes the university equivalent to a small city, and its network infrastructure is what keeps it all connected.

Their small IT team was responsible for maintaining more than 10,000 management devices, most of which were long past EOL and frequently failing. They needed a refresh, but with a solution that could also reduce the hundreds of hours they spent every month on travel and on-site work. To maximize their day-to-day efficiency, they required a solution that could overcome these operational gaps:

• Reducing the 100-150 hours of monthly travel times, by giving engineers the ability to fully access their stack remotely
• Reducing the 80-120 hours of monthly on-site work required to maintain the 99.999% SLA, by automating manual jobs such as patching and firmware upgrades
• Expanding their management headroom and use-case adaptability, by migrating to IPv6 and reducing the existing 6RU device stack

Download the full case study to see how ZPE’s Nodegrid hardware and software solved these problems.

Problems and Gaps

The university is one of the largest in the United States. It sits among the nation’s top 50 schools for research expenditures, and heavily integrates technology into all disciplines, including engineering. Its main campus is home to more than 200 buildings that sit on over 2,500 acres of land. The campus is essentially a small city, and the university’s network infrastructure keeps it all connected.

This network infrastructure, however, was well beyond EOL and in disrepair. But rather than simply upgrade to newer devices, the university’s small IT team wanted to improve the overall quality of life well into the future. This meant addressing three gaps:

• Inefficient management at scale — Each engineer spent an average of ten hours per month on travel alone, just to traverse the campus’ wide footprint and get to each MDF/IDF closet.
• Too much focus on ops — The aging infrastructure was on the brink of collapse and required each engineer to spend eight hours per month in on-site work, just to keep devices running.
• Too many devices — The infrastructure includes roughly 10,000 devices to manage, which was exhausting IP on their limited IPv4 network and too rigid to fit in tight spaces, like their remote farm closets and research labs.

Solution

The university deployed the full lineup of Nodegrid devices, including the Nodegrid Serial Console, Nodegrid Services Routers, and Nodegrid Manager. These allowed them to overcome all three gaps using remote management, automation, and consolidated functionality, to save engineers hundreds of hours every month. Download the full case study to see the complete solution and benefits.

Need Help Replacing End-of-Life Gear?

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Service providers often struggle with the hybrid nature of their business. Even as they transition more towards a consumable service-based model that’s decoupled from traditional hardware solutions, there’s still a need for some sort of box to be deployed physically at a customer’s premises. Providers frequently rely on COTS (Common Off The Shelf) hardware to reduce costs and simplify the deployment process.

One commonly used COTS device is the Intel NUC, or “Next Unit of Computing,” which is a small appliance-like mini computer. Some service providers utilize Intel NUC devices as jump boxes, while others use them as a platform to deploy their services on-site. While these mini-computers are relatively inexpensive and easy to install, they create added security risks and management headaches that service providers need to be aware of.

This post highlights the challenges and security risks involved in relying on Intel NUC devices before discussing enterprise-grade Intel NUC alternatives that solve these problems.

Why is Intel NUC so popular in IT infrastructure?

Managed Service Providers (MSPs) and Managed Security Service Providers (MSSPs) often use Intel NUC jump boxes to remotely access the control plane of critical client infrastructure. These mini PCs typically run bare bones software to reduce licensing costs, which means they are unpatched, unmonitored, and unsecured. This lack of oversight and management makes Intel NUCs popular access points for hackers to breach client networks.

Why consider Intel NUC alternatives?

Service providers like to use Intel NUC boxes because they’re cheaper, faster to install, and take up less space than a full PC or server. NUCs are often deployed without antivirus, monitoring agents, or other security software installed, which excludes them from the service provider’s security coverage. Plus, clients are frequently unaware that these devices are in their racks accessing their infrastructure, so they don’t access them in security and compliance audits. Other Intel NUC challenges include:

• Lack of centralized management – Each Intel NUC is an island that’s managed and accessed individually, which makes it impossible to efficiently deploy updates, install new tools, or monitor for problems.
• Insecure, unpatched OS – Operating systems and software contain thousands of potential vulnerabilities that hackers can exploit, so a lack of monitoring and patch management creates a huge security risk.
• No hardware security – Intel NUC boxes lack any hardware security, which means someone could steal the device and use it to deploy malware or access client resources – or even just pawn the hardware.
• Regulatory issues – When providers use unmanaged jump boxes to access client infrastructure, they expose their customers to potential noncompliance with privacy laws like HIPAA that require strict data access controls.
• Affects insurance eligibility – Using an unsecured Intel NUC may also disqualify customers from receiving cybersecurity insurance benefits in the event of a successful breach.

While Intel NUCs are a quick and inexpensive way for MSPs, MSSPs, and other service providers to remotely access client infrastructure, they also make it easier for cybercriminals to breach enterprise networks. To reduce the attack surface without increasing the cost, hassle, or footprint of deploying jump boxes, you need an enterprise-grade solution that combines networking functions, security, and remote out-of-band access to the control plane to eliminate the need for a separate device.

Intel NUC alternatives from ZPE Systems

The Nodegrid product line from ZPE Systems simplifies the tech stack in data centers and network closets with all-in-one infrastructure management solutions. Nodegrid devices roll up gateway routing, switching, Wi-Fi, and 5G/4G/LTE out-of-band management to cut down on the number of boxes in the rack. They’re also enterprise solutions, which means they can be onboarded with your security team and covered by your monitoring, intrusion detection, antivirus, and other security controls.

In addition, all Nodegrid boxes are protected by hardware security features such as BIOS protection, self-encrypted disk (SED), UEFI Secure Boot, and Signed OS. Plus, Nodegrid’s hardware and software are completely vendor-neutral, allowing easy integrations with third-party security solutions and SAML 2.0 authentication. Nodegrid can even directly host other vendors’ security software to further reduce your tech stack.

Key Nodegrid features

To learn more about the benefits of Nodegrid’s Intel NUC alternatives, contact ZPE Systems.

Nodegrid product comparison

The Nodegrid family of network edge routers delivers secure, Gen 3 OOB management for reliable remote access to distributed customer sites like branch offices or manufacturing centers.

Nodegrid Service Delivery Platform Family

The Nodegrid family of Intel NUC alternatives from ZPE Systems can help MSPs and MSSPs ensure secure, reliable remote management access to customer infrastructure without increasing costs.

Ready for a Demo?

To see one of ZPE’s Intel NUC alternatives in action, request a free Nodegrid demo! Request a Demo