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

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

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

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

What is an Edge Computing Platform (ECP)?

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

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

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

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

1. Edge Diversity

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

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

2. Ongoing Digital Transformation

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

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

3. Data Growth

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

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

4. Business-Led Requirements

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

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

5. Technology Focus

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

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

What Features Do Edge Computing Platforms Need to Have?

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

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

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

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

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How ZPE Systems’ Nodegrid Platform Addresses Edge Computing Challenges

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

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

1. Edge Diversity: Adapting to Industry-Specific Needs

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

2. Ongoing Digital Transformation: Supporting Continuous Growth

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

3. Data Growth: Enabling Real-Time Data Processing

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

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

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

5. Technology Focus: Supporting Advanced AI/ML Applications

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

Read Gartner’s Market Guide for Edge Computing Platforms

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

Click below to download the report.

Download Market Guide

Get a Demo of Nodegrid’s Secure Service Delivery

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

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Network Virtualization Platforms: Benefits & Best Practices

by | Oct 11, 2024 | Application Hosting, Consolidation, EdgeOps, Improve Network Security, Out of Band Management, Remote Network Management, SD-WAN, Secure Access Service Edge (SASE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Virtualization

Network Virtualization Platforms: Benefits & Best Practices

Simulated network virtualization platforms overlaying physical network infrastructure.

Network virtualization decouples network functions, services, and workflows from the underlying hardware infrastructure and delivers them as software. In the same way that server virtualization makes data centers more scalable and cost-effective, network virtualization helps companies streamline network deployment and management while reducing hardware expenses.

This guide describes several types of network virtualization platforms before discussing the benefits of virtualization and the best practices for improving efficiency, scalability, and ROI.

What do network virtualization platforms do?

There are three forms of network virtualization that are achieved with different types of platforms. These include:

Type of Virtualization Description Examples of Platforms
Virtual Local Area Networking (VLAN) Creates an abstraction layer over physical local networking infrastructure so the company can segment the network into multiple virtual networks without installing additional hardware.

SolarWinds Network Configuration Manager

ManageEngine Network Configuration Manager
Software-Defined Networking (SDN) Decouples network routing and control functions from the actual data packets so that IT teams can deploy and orchestrate workflows across multiple devices and VLANs from one centralized platform.

Meraki

Juniper
Network Functions Virtualization (NFV) Separates network functions like routing, switching, and load balancing from the underlying hardware so teams can deploy them as virtual machines (VMs) and use fewer physical devices.

Red Hat OpenStack

VMware vCloud NFV

While network virtualization is primarily concerned with software, it still requires a physical network infrastructure to serve as the foundation for the abstraction layer (just like server virtualization still requires hardware in the data center or cloud to run hypervisor software). Additionally, the virtualization software itself needs storage or compute resources to run, either on a server/hypervisor or built-in to a networking device like a router or switch. Sometimes, this hardware is also referred to as a network virtualization platform.

The benefits of network virtualization

Virtualizing network services and workflows with VLANs, SDN, and NFVs can help companies:

  • Improve operational efficiency with automation. Network virtualization enables the use of scripts, playbooks, and software to automate workflows and configurations. Network automation boosts productivity so teams can get more work done with fewer resources.
  • Accelerate network deployments and scaling. Legacy deployments involve configuring and installing dedicated boxes for each function. Virtualized network functions and configurations can be deployed in minutes and infinitely copied to get new sites up and running in a fraction of the time.
  • Reduce network infrastructure costs. Decoupling network functions, services, and workflows from the underlying hardware means you can run multiple functions from once device, saving money and space.
  • Strengthen network security. Virtualization makes it easier to micro-segment the network and implement precise, targeted Zero-Trust security controls to protect sensitive and valuable assets.

Network virtualization platform best practices

Following these best practices when selecting and implementing network virtualization platforms can help companies achieve the benefits described above while reducing hassle.

Vendor neutrality

Ensuring that the virtualization software works with the underlying hardware is critical. The struggle is that many organizations use devices from multiple vendors, which makes interoperability a challenge. Rather than using different virtualization platforms for each vendor, or replacing perfectly good devices with ones that are all from the same vendor, it’s much easier and more cost-effective to use virtualization software that interoperates with any networking hardware. This type of software is called ‘vendor neutral.’

To improve efficiency even more, companies can use vendor-neutral networking hardware to host their virtualization software. Doing so eliminates the need for a dedicated server, allowing SDN software and virtualized network functions (VNFs) to run directly from a serial console or router that’s already in use. This significantly consolidates deployments, which saves  money and reduces the amount of space needed This can be a lifesaver in branch offices, retail stores, manufacturing sites, and other locations with limited space.

A diagram showing how multiple VNFs can run on a single vendor-neutral platform.

Virtualizing the WAN

We’ve mostly discussed virtualization in a local networking context, but it can also be extended to the WAN (wide area network). For example, SD-WAN (software-defined wide area networking) streamlines and automates the management of WAN infrastructure and workflows. WAN gateway routing functions can also be virtualized as VNFs that are deployed and controlled independently of the physical WAN gateway, significantly accelerating new branch launches.

Unifying network orchestration

The best way to maximize network management efficiency is to consolidate the orchestration of all virtualization with a single, vendor-neutral platform. For example, the Nodegrid solution from ZPE Systems uses vendor-neutral hardware and software to give networking teams a single platform to host, deploy, monitor, and control all virtualized workflows and devices. Nodegrid streamlines network virtualization with:

  • An open, x86-64bit Linux-based architecture that can run other vendors’ software, VNFs, and even Docker containers to eliminate the need for dedicated virtualization appliances.
  • Multi-functional hardware devices that combine gateway routing, switching, out-of-band serial console management, and more to further consolidate network deployments.
  • Vendor-neutral orchestration software, available in on-premises or cloud form, that provides unified control over both physical and virtual infrastructure across all deployment sites for a convenient management experience.

Want to see vendor-neutral network orchestration in action?

Nodegrid unifies network virtualization platforms and workflows to boost productivity while reducing infrastructure costs. Schedule a free demo to experience the benefits of vendor-neutral network orchestration firsthand.

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Edge Computing Use Cases in Banking

by | Aug 13, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

financial services

The banking and financial services industry deals with enormous, highly sensitive datasets collected from remote sites like branches, ATMs, and mobile applications. Efficiently leveraging this data while avoiding regulatory, security, and reliability issues is extremely challenging when the hardware and software resources used to analyze that data reside in the cloud or a centralized data center.

Edge computing decentralizes computing resources and distributes them at the network’s “edges,” where most banking operations take place. Running applications and leveraging data at the edge enables real-time analysis and insights, mitigates many security and compliance concerns, and ensures that systems remain operational even if Internet access is disrupted. This blog describes four edge computing use cases in banking, lists the benefits of edge computing for the financial services industry, and provides advice for ensuring the resilience, scalability, and efficiency of edge computing deployments.

4 Edge computing use cases in banking

1. AI-powered video surveillance

PCI DSS requires banks to monitor key locations with video surveillance, review and correlate surveillance data on a regular basis, and retain videos for at least 90 days. Constantly monitoring video surveillance feeds from bank branches and ATMs with maximum vigilance is nearly impossible for humans, but machines excel at it. Financial institutions are beginning to adopt artificial intelligence solutions that can analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security personnel.

When these AI-powered surveillance solutions are deployed at the edge, they can analyze video feeds in real time, potentially catching a crime as it occurs. Edge computing also keeps surveillance data on-site, reducing bandwidth costs and network latency while mitigating the security and compliance risks involved with storing videos in the cloud.

2. Branch customer insights

Banks collect a lot of customer data from branches, web and mobile apps, and self-service ATMs. Feeding this data into AI/ML-powered data analytics software can provide insights into how to improve the customer experience and generate more revenue. By running analytics at the edge rather than from the cloud or centralized data center, banks can get these insights in real-time, allowing them to improve customer interactions while they’re happening.

For example, edge-AI/ML software can help banks provide fast, personalized investment advice on the spot by analyzing a customer’s financial history, risk preferences, and retirement goals and recommending the best options. It can also use video surveillance data to analyze traffic patterns in real-time and ensure tellers are in the right places during peak hours to reduce wait times.

3. On-site data processing

Because the financial services industry is so highly regulated, banks must follow strict security and privacy protocols to protect consumer data from malicious third parties. Transmitting sensitive financial data to the cloud or data center for processing increases the risk of interception and makes it more challenging to meet compliance requirements for data access logging and security controls.

Edge computing allows financial institutions to leverage more data on-site, within the network security perimeter. For example, loan applications contain a lot of sensitive and personally identifiable information (PII). Processing these applications on-site significantly reduces the risk of third-party interception and allows banks to maintain strict control over who accesses data and why, which is more difficult in cloud and colocation data center environments.

4. Enhanced AIOps capabilities

Financial institutions use AIOps (artificial intelligence for IT operations) to analyze monitoring data from IT devices, network infrastructure, and security solutions and get automated incident management, root-cause analysis (RCA), and simple issue remediation. Deploying AIOps at the edge provides real-time issue detection and response, significantly shortening the duration of outages and other technology disruptions. It also ensures continuous operation even if an ISP outage or network failure cuts a branch off from the cloud or data center, further helping to reduce disruptions and remote sites.

Additionally, AIOps and other artificial intelligence technology tend to use GPUs (graphics processing units), which are more expensive than CPUs (central processing units), especially in the cloud. Deploying AIOps on small, decentralized, multi-functional edge computing devices can help reduce costs without sacrificing functionality. For example, deploying an array of Nvidia A100 GPUs to handle AIOps workloads costs at least $10k per unit; comparable AWS GPU instances can cost between $2 and $3 per unit per hour. By comparison, a Nodegrid Gate SR costs under $5k and also includes remote serial console management, OOB, cellular failover, gateway routing, and much more.

The benefits of edge computing for banking

Edge computing can help the financial services industry:

  • Reduce losses, theft, and crime by leveraging artificial intelligence to analyze real-time video surveillance data.
  • Increase branch productivity and revenue with real-time insights from security systems, customer experience data, and network infrastructure.
  • Simplify regulatory compliance by keeping sensitive customer and financial data on-site within company-owned infrastructure.
  • Improve resilience with real-time AIOps capabilities like automated incident remediation that continues operating even if the site is cut off from the WAN or Internet
  • Reduce the operating costs of AI and machine learning applications by deploying them on small, multi-function edge computing devices. 
  • Mitigate the risk of interception by leveraging financial and IT data on the local network and distributing the attack surface.

Edge computing best practices

Isolating the management interfaces used to control network infrastructure is the best practice for ensuring the security, resilience, and efficiency of edge computing deployments. CISA and PCI DSS 4.0 recommend implementing isolated management infrastructure (IMI) because it prevents compromised accounts, ransomware, and other threats from laterally moving from production resources to the control plane.

IMI with Nodegrid(2)

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.

Edge-Management-980×653

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 Orchestration: Solving Challenges to Improve AI Value

by | Aug 2, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

AI Orchestration(1)
Generative AI and other artificial intelligence technologies are still surging in popularity across every industry, with the recent McKinsey global survey finding that 72% of organizations had adopted AI in at least one business function. In the rush to capitalize on the potential productivity and financial gains promised by AI solution providers, technology leaders are facing new challenges relating to deploying, supporting, securing, and scaling AI workloads and infrastructure. These challenges are exacerbated by the fragmented nature of many enterprise IT environments, with administrators overseeing many disparate, vendor-specific solutions that interoperate poorly if at all.

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.

A diagram showing isolated management infrastructure with the Nodegrid Serial Console Plus.

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

Edge Computing Use Cases in Telecom

by | Jul 31, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for the telecommunications industry.
Telecommunications networks are vast and extremely distributed, with critical network infrastructure deployed at core sites like Internet exchanges and data centers, business and residential customer premises, and access sites like towers, street cabinets, and cell site shelters. This distributed nature lends itself well to edge computing, which involves deploying computing resources like CPUs and storage to the edges of the network where the most valuable telecom data is generated. Edge computing allows telecom companies to leverage data from CPE, networking devices, and users themselves in real-time, creating many opportunities to improve service delivery, operational efficiency, and resilience.

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.

IMI with Nodegrid(2)
To ensure the scalability and flexibility of edge architectures, the best practice is to use vendor-neutral platforms to host, connect, and secure edge applications and workloads. Moving away from dedicated device stacks and taking a “platformization” approach allows organizations to easily deploy, update, and swap out functions and services on demand. For example, Nodegrid edge networking solutions have a Linux-based OS that supports third-party VMs, Docker containers, and NFVs. Telecom companies can use Nodegrid to run edge computing workloads as well as asset management software, customer experience analytics, AIOps, and edge security solutions like SASE.

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.

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Finally, to ensure teams have holistic oversight of the distributed edge computing architecture, the best practice is to use a centralized, cloud-based edge management and orchestration (EMO) platform. This platform should also be vendor-neutral to ensure complete coverage and should use out-of-band management to provide continuous management access to edge infrastructure even during a major service outage.

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 Use Cases in Retail

by | Jul 25, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

Automated transportation robots move boxes in a warehouse, one of many edge computing use cases in retail
Retail organizations must constantly adapt to meet changing customer expectations, mitigate external economic forces, and stay ahead of the competition. Technologies like the Internet of Things (IoT), artificial intelligence (AI), and other forms of automation help companies improve the customer experience and deliver products at the pace demanded in the age of one-click shopping and two-day shipping. However, connecting individual retail locations to applications in the cloud or centralized data center increases network latency, security risks, and bandwidth utilization costs.

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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Edge computing benefits

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Reduced latency

Edge computing decreases the number of network hops between devices and the applications they rely on, reducing latency and improving the speed and reliability of retail technology at the edge.

Real-time insights

Edge computing can analyze data in real-time and provide actionable insights to improve the customer experience before a sale is lost or reduce waste before monthly targets are missed.

Improved resilience

Edge computing applications can continue functioning even if the site loses Internet or WAN access, enabling continuous operations and reducing the costs of network downtime.

Risk mitigation

Keeping sensitive internal data like personnel records, sales numbers, and customer loyalty information on the local network mitigates the risk of interception and distributes the attack surface.
Edge computing can also help retail companies lower their operational costs at each site by reducing bandwidth utilization on expensive MPLS links and decreasing expenses for cloud data storage and computing. Another way to lower costs is by using consolidated, vendor-neutral solutions to run, connect, and secure edge applications and workloads.

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.

Watch a demo