Most organizations rely on critical IT in order to serve their essential business functions. A reliable method to maintain critical IT is to use dedicated out-of-band (OOB) management networks, which traditionally have relied on plain old telephone service (POTS) lines or dedicated telephony circuits for remote access. However, these traditional links come with high costs, lots of complexity, and slow performance, which make them difficult to deploy and maintain.

Enter Starlink, a satellite-based Internet service that offers a cost-effective and scalable alternative for out-of-band remote access. This post discusses how Starlink solves these common problems and gives you a free guide that walks you through the setup process.

Problem: POTS and Telephony Lines Are Expensive

For decades, IT professionals have relied on POTS and telephony lines for OOB management, mainly because these lines remain operational even when the primary data network goes down. A major problem is that POTS lines are increasingly expensive to install and maintain, particularly in remote or rural areas. Additionally, 4G/5G LTE options aren’t always available due to coverage limitations or large enough data plans. The shift towards VoIP (Voice over IP) and digital communications has made POTS lines even less relevant, with many service providers phasing out support. This leaves businesses with fewer options and higher costs for maintaining these legacy systems.

Solution: Starlink is Cost-Effective

Starlink offers a much more cost-effective solution. You can use off-the-shelf routers to set up an OOB management network for a fraction of the cost of traditional methods. Starlink also has a relatively low monthly subscription fee and straightforward pricing model, which make it easy to budget and plan IT expenditures. If components fail or break, you can typically repair or replace them yourself to get back up and running quickly.

Figure 1: Starlink requires only a dish, router, and few other components, making it a cost-effective alternative to expensive POTS lines.

Problem: Traditional Lines Are Difficult To Scale

Traditional POTS-based systems are notoriously difficult to scale, often requiring significant infrastructure investments and complex configurations. Copper wiring is expensive to install and maintain, and as more connections come online, switching systems must be upgraded. On top of this, POTS lines are being phased out, which means there are fewer resources being devoted to scaling and maintaining them.

Solution: Starlink is Simple to Set Up and Scale

Starlink entirely eliminates the need for telephony lines, and is a simple and scalable solution for OOB remote access. You can find the full list of components in our setup guide below, but with a Starlink terminal, compatible router, and minimal configuration, you can scale your OOB network wherever you have Starlink coverage. This ease-of-use extends to day-to-day management as well. Starlink’s satellite service offers global coverage, meaning you can manage your network devices, servers, and other critical infrastructure from virtually anywhere in the world.

Figure 2: Starlink comes with a straightforward out-of-box experience and step-by-step instructions. You can set up an out-of-band network in about one hour.

Problem: POTS Lines Lack Performance

POTS is designed primarily for voice communication and offers extremely limited bandwidth. It can’t support modern data services (such as video or high-speed internet) efficiently. As out-of-band management advances with data and video monitoring capabilities (such as AI computer vision), POTS infrastructure just doesn’t have the bandwidth to keep up.

Solution: Starlink Meets Modern Performance Requirements

Starlink provides high-speed internet, at speeds that typically range from 50 to 200Mbps. The connection handles much larger volumes of data than POTS lines are capable of, and Starlink’s low-Earth orbit satellites reduce latency to as low at 25ms compared to the typical 150ms of POTS lines. Out-of-band using Starlink means that IT teams can manage more systems and data, and have a more responsive experience, whether they’re managing edge routers across their bank branches or monitoring the cooling systems in their distributed colocations.

Figure 3: Starlink provides high-speed connectivity, with speeds ranging from 50 to 200Mbps.

Get Started With Starlink Using Our Setup Guide

What is Passive Optical Networking (PON)?

Passive optical networking (PON) is a high-speed broadband technology that enables the delivery of multiple services over a single fiber optic cable. XGS-PON – 10G Symmetrical PON –  offers speeds of up to 10 Gbps downstream and 10 Gbps upstream (hence the term ‘symmetrical’), making it ideal for applications such as video streaming, online gaming, and cloud computing.

What Problems Does PON Solve for Out-of-Band Management?

PON addresses the issue of efficiency in terms of both uplink costs and bandwidth usage. Traditional POTS lines and dedicated circuits rely on legacy infrastructure that requires regular maintenance. This infrastructure must scale as more out-of-band devices are added to the network, which increases costs and energy consumption. On top of this, using a 10G uplink for a serial console’s 10K traffic is like throwing away 99% of that high bandwidth. Per Gartner’s Market Guide for Optical Transport Systems report (Published 20 November 2023) the best way to “lower cost and energy per transported bit” is by using technologies such as passive optical networking.

Because PON uses passive optical splitters that have no moving parts or powered components between the central hub and end users, PON is much more efficient for deploying serial consoles close to target assets. These out-of-band devices can be deployed in large quantities and close to the network edge, with up to 256 devices sharing one uplink. This reduces cabling and power requirements, and is ideal for MSP and campus operators, where there are many out-of-band devices distributed over long distances. 

More About PON: GPON and XGS-PON Technologies

Passive Optical Networking (PON) leverages time-division multiplexing (TDM) and different wavelengths of light to transmit and receive data on a single fiber strand. This allows efficient communication among up to 256 devices over a single fiber. Initially developed for fiber-to-the-home (FTTH) deployments, PON technology has evolved to facilitate the addition of network nodes with minimal infrastructure changes. GPON (gigabit-capable PON) and XGS-PON use different frequencies for upstream and downstream data transmission. The upstream headend, known as the Optical Line Terminal (OLT), manages and coordinates the time slots allocated to downstream Optical Network Units (ONUs) for data transmission.

GPON and XGS-PON Support on ZPE Systems’ Nodegrid SR Gateway

ZPE Systems’ Nodegrid SR appliances, which are used as out-of-band access nodes or complete branch gateways, now support GPON and XGS-PON technology (patent pending) via SFP and SFP+ ports. The Nodegrid SR family is offered in multiple form factors to be right-sized for deployments in branch offices, factories, smart buildings, and industrial environments (such as for SCADA).

Having support for GPON and XGS-PON means network engineers now have a flexible choice of high-speed uplink technologies. This versatility makes the Nodegrid SR gateway suitable for edge deployments, where it can establish an OOBI-WAN (out-of-band infrastructure WAN) link, and for data centers, where it enhances uplink efficiency. Given the low bandwidth requirements of serial console and out-of-band communications, PON technology is well-suited for these applications. A single fiber strand can be shared among hundreds of out-of-band and serial console devices using passive optical splitters. Organizations can deploy out-of-band devices close to the racks and edges of the network in a cost- and energy-efficient manner. Additionally, ZPE devices support ONU SFPs compatible with third-party OLT headends, ensuring broad interoperability and integration.

Benefits of Using XGS-PON with ZPE Systems’ Nodegrid SR Gateway

The benefits of using XGS-PON with ZPE Systems’ Nodegrid SR gateway include:

• High-Speed Connectivity: XGS-PON delivers symmetrical speeds of up to 10 Gbps, making it ideal for high-bandwidth applications like video streaming, online gaming, and cloud computing. This ensures consistent and high-quality service for end-users.
• Cost-Effectiveness: Deploying XGS-PON is a cost-effective solution for delivering high-speed broadband services, especially in scenarios where upgrading existing infrastructure may be challenging.
• Scalability: The Nodegrid SR Gateway, acting as an ONU, can connect up to 256 serial consoles through a single fiber strand. PON’s use of asymmetric wavelengths and TDM enables multiple devices to share the same fiber strand efficiently. Optical splitters, which require no external power, facilitate the sharing of fiber between multiple ONUs, which makes scaling much more cost and energy efficient.
• Reliability: The Nodegrid SR gateway is proven by service providers worldwide. Its robust design and compatibility with various network configurations make it a reliable choice for delivering high-quality broadband services.

Figure 1: ZPE Nodegrid SR gateway with XGS-PON ONU support

XGS-PON Enhances Efficiency of Out-of-Band

XGS-PON is a significant advancement over traditional, copper-based uplinks. The integration of XGS-PON support in the ZPE Systems Nodegrid SR Gateway allows network architects to deploy a dedicated out-of-band ring that is not only high-speed but also cost-effective, energy-efficient, and capable of covering longer distances. PON technology, with its ability to handle the lower data rates of out-of-band transmissions, is an ideal uplink medium for serial console transmission. The combination of XGS-PON and the Nodegrid SR Gateway provides a powerful and flexible solution for modern network infrastructure.

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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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