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

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

How Nodegrid streamlines edge computing for the banking industry

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

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

Get Nodegrid for your edge computing use cases in banking

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

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

AI challenges to overcome

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

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

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

How AI orchestration streamlines AI/ML in an enterprise environment

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

Automation

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

Unification

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

Resilience

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

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

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

Getting the most out of your AI investment

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

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

Streamline AI orchestration with Nodegrid

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

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

4 Edge computing use cases in telecom

1. Enhancing the customer experience with real-time analytics

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

2. Streamlining remote infrastructure management and recovery with AIOps

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

3. Preventing environmental conditions from damaging remote equipment

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

4. Improving operational efficiency with network virtualization and consolidation

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

Edge computing in telecom: Benefits and best practices

Edge computing can help telecommunications companies:

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

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

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

Streamlined, cost-effective edge computing with Nodegrid

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

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

5 Edge computing use cases in retail

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1. Security video analysis

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

2. Localized, real-time insights

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

3. Enhanced inventory management

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

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

5. Warehouse automation

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

The benefits of edge computing for retail

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

Consolidated edge computing with Nodegrid

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

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Secure access service edge (SASE) is the recommended architecture for security and connectivity.  SASE combines wide area network (WAN) technology for robust onramp to cloud and network security services into one cloud-delivered connectivity and security software stack. This allows enterprises to connect geographically diverse workforces securely while reducing network latency and performance issues. 

Though SASE is a relatively new concept, it’s taking the IT world by storm, partially due to the pandemic forcing companies to adopt or improve their remote work capabilities. In addition, SASE addresses the security challenges of using WAN and SD-WAN (software-defined wide area network) technology for remote and branch office (ROBO) network management. 

Let’s examine two essential SASE model use cases and discuss the benefits of integrating SASE into your enterprise network management and security strategy.

SASE model key use cases and benefits

SASE offers numerous benefits for remote and branch office security, performance, and network management, which may be why Gartner predicts that at least 40% of enterprises will have explicit plans for SASE adoption by 2024. Consider these use cases as you decide whether adopting the SASE model aligns with your business goals and network management and security requirements.

SASE use case #1: Replacing VPNs for remote work

The need to pivot to a remote workforce in 2020 has driven many organizations to prioritize SASE adoption. Enterprises use VPNs (virtual private networks) to handle their limited work-from-home traffic. But scaling up a VPN solution with enough licenses and VPN concentrators to meet an entirely remote workforce’s increased demand can be more expensive. 

Additionally, not all VPN services include centralized remote management to deploy, monitor, and manage remote connections. This could be a minor issue if you only have a handful of remote employees at any given time, but a substantial logistical challenge when your entire workforce must suddenly pivot to work from home. 

If you were relying on a VPN solution for all remote work, you likely found yourself overwhelmed by the need to deploy and troubleshoot hundreds or thousands of new VPN client installations, keep those connections secure without crippling your network performance, and ensure that all your enterprise and cloud applications were tested and supported for VPN access.

SASE model benefits of replacing VPNs for remote work

SASE implementations can solve a lot of these remote work challenges. Instead of creating an encrypted tunnel between each remote workstation and your primary network, like a VPN, SASE connects remote users to nearby points of presence (PoPs) to access enterprise applications and resources in the cloud or the data center. 

All traffic to and from a PoP is encrypted, with other security technologies—such as secure web gateways (SWGs), remote browser isolation, and cloud firewalls—layered to monitor and protect system use. SASE provides additional security by using cloud access security brokers (CASBs) to apply enterprise access control policies to resources outside of the data center, such as Software as a Service (SaaS) tools or other cloud applications.

Despite these robust security controls, SASE still reduces network latency and improves application performance for remote workers compared to a VPN. Instead of relying on a limited number of VPN gateways to handle all your remote traffic, SASE uses a wide network of PoPs to connect remote users to the services and applications they need. 

If a remote user needs to access a cloud application, a PoP can connect them directly to that service, bypassing your data centers and reducing the load on your network. In addition, many SASE providers house their PoPs in the same facilities as major SaaS providers—Microsoft 365 and Salesforce, for example—optimizing the routing paths to these applications and improving performance for remote workers.

IT teams may find SASE easier to manage than VPNs as well. One of SASE’s big selling points for engineers and security teams is reduced network complexity—SASE seeks to replace the physical and virtual VPN appliances you use for remote traffic with a single cloud-native solution. One main advantage is that the end user experience is at its best since the traffic can reach the destination quickly without tromboning (hairpinning) through the datacenter and competing for bandwidth with increased latency. 

This also reduces the amount of time and resources spent on updates and patching, device maintenance, and configuration management for your VPN appliances and other remote and branch network infrastructure. SASE also provides one centralized management platform to control identity management and security policies for the entire enterprise and monitor and manage remote network traffic.

Replacing VPNs with SASE for your remote workforce improves the security of your remote traffic and systems, reduces network latency, increases SaaS and cloud application performance, and simplifies remote network and security management.

SASE use case #2: Optimizing SD-WAN security and performance

Many enterprises have already jumped from VPN and traditional WAN technology to SD-WAN or software-defined vast area networks. SD-WAN improves upon WAN technology—often using existing public and private WAN connections as a backbone or underlay network—to connect remote workers and branch offices to enterprise services and applications. 

SD-WAN separates the control and management processes from the underlying WAN hardware and makes those functions available as software (hence the name “software-defined” WAN). This virtualized overlay network creates a private, encrypted WAN to connect branch locations, prioritize and route ROBO traffic, and manage and monitor network performance.

SD-WAN does present some security challenges, however. An SD-WAN implementation requires the use of firewalls, intrusion prevention, and web filtering at each branch office, which could mean installing and configuring hundreds or thousands of security appliances. Cyberattacks are becoming a more significant threat each year, reportedly costing businesses up to $4 billion in 2020, so many enterprises are looking to a security-centric solution like SASE to protect their network edge. SASE essentially combines SD-WAN functionality with network security features and bundles them together as a single solution.

SASE model benefits of optimizing SD-WAN security and performance

SASE allows teams to manage both SD-WAN traffic and security from a single pane of glass. SASE solutions roll up security features like CASB, firewall as a service (FWaaS), and zero trust network access (ZTNA) into a single cloud-native service to prevent, detect and mitigate network attacks without the need to deploy multiple security appliances and solutions for all your branch sites. 

For existing SD-WAN implementations, you can layer SASE’s network security features into the WAN appliances at each branch office to provide next generation firewall, intrusion protection, analytics, and unified threat management functionality without purchasing new infrastructure. This means you can manage the security of all your branch locations without needing to install firewalls and other security appliances at each site, reducing network complexity by combining SD-WAN and security into one centrally managed solution.

Plus, since the SASE model connects remote and branch users with SaaS and cloud applications via PoPs, you won’t need to backhaul your branch office traffic through your leading network’s firewall. This means your external-to-external traffic (from branch sites to cloud services and vice versa) bypasses your primary network entirely, reducing bottlenecks and delays and improving network and application performance.

You can use SASE to integrate cloud-based security functionality like CASB, FWaaS, and ZTNA with your existing SD-WAN infrastructure, or you can use SASE’s combined security and SD-WAN service stack to upgrade a traditional WAN architecture. Either way, you’ll reduce network complexity and provide a centralized solution for managing ROBO network traffic and security, all while reducing network bottlenecks and application performance issues.

Take complete advantage of all SASE model benefits

Two of the biggest use cases driving enterprises to adopt SASE include the recent pivot to a remote, home-based workforce and the need to improve the security and management of WAN and SD-WAN technology for branch offices.

The SASE model combines SD-WAN technology with network security features into a unified, cloud-native service stack to provide enterprises with many benefits, including increased security, improved application, network performance, and simplified management for remote and branch office connections.  

To realize a SASE architecture organizations need a robust and extensible branch edge device that can be the ‘Access’ on-ramp to the cloud delivered ‘Secure Service Edge’ (SSE.)

ZPE Systems’ Nodegrid family of hardware and software is a modular, vendor-neutral solution that provides innovative features such as 4G/LTE failover to maintain business continuity, remote out-of-band management (OOBM) for greater device visibility, and zero touch provisioning (ZTP) to automate deployment.  And our SR family can be the on-ramp to SSE vendors such as zScaler, Netscope, Acreto or similar.  Contact us for a deep dive video demo of our solution providing the Access onramp for SSE to flexibly realize the SASE architecture. 

ZPE Systems’ Nodegrid platform is a comprehensive branch networking solution that supports a comprehensive SASE model platform. 

To learn more about how Nodegrid’s built-in automation and ROBO management features can streamline your SASE deployment, get in touch with ZPE Systems today.

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