Providing Out-of-Band Connectivity to Mission-Critical IT Resources

The Future of Data Centers: Overcoming the Challenges of Lights-Out Operations

Future of lights-out data centers

In a recent article, Y Combinator announced its search for startups aiming to eliminate human intervention in data center development and operation. While one half of this vision seems focused on automating the design and construction of data centers, the other half – focused on fully automating operations (a.k.a. “lights-out”) – is already a reality. ZPE Systems and Legrand are enabling enterprises to achieve this kind of operation by providing the best practices that are already in use in hyperscale data centers for lights-out management.

The Need for Lights-Out Data Centers

The growth of cloud computing, edge deployments, and AI-driven workloads means data centers need to be as efficient, scalable, and resilient as possible. The challenge is that because there is so much infrastructure to manage, the buildout and operation of these data centers becomes very costly and time consuming.

Diane Hu, a YC group partner who previously worked in augmented reality and data science, says, “Hyperscale data center projects take many years to complete. We need more data centers that are created faster and cheaper to build out the infrastructure needed for AI progress. Whether it be in power infrastructure, cooling, procurement of all materials, or project management.”

Dalton Caldwell, a YC managing director who also cofounded App.net, adds, “Software is going to handle all aspects of planning and building a new data center or warehouse. This can include site selection, construction, set up, and ongoing management. They’re going to be what’s called lights-out. There’s going to be robots, autonomously operating 24/7. We want to fund startups to help create this vision.”

In terms of ongoing management and operations, bringing this vision to life will require organizations to overcome several significant problems:

  1. Rising Operational Costs: Staffing and maintaining on-site engineers 24/7 is costly. Labor expenses, training, and turnover increase operational overhead.
  2. Human Error and Downtime: Human error is the leading cause of downtime, so having manual processes often leads to costly outages caused by typos, misconfigurations, and slow response times.
  3. Security Threats: Physical access to data centers increases the risk of insider threats, breaches, and unauthorized interventions.
  4. Remote Site Management: Managing geographically distributed data centers and edge locations requires staff to be on-site. What’s needed is a scalable and efficient solution that lets staff remotely perform every job, outside of physically installing equipment.
  5. Sustainability and Energy Efficiency: On-site workers have specific heating/cooling needs that must be met in order to comfortably perform their jobs. Reducing human presence in data centers enables better energy management, which can lower carbon footprints and reduce cooling requirements.

The Roadblocks to Lights-Out Data Centers

Despite the obvious benefits, organizations struggle to implement fully autonomous data center operations. The obstacles include:

  • Legacy Infrastructure: Many enterprises still rely on outdated equipment that lacks the necessary integrations for automation and remote control. Adding functions or capabilities typically means deploying more physical boxes, which increases costs and complexity.
  • Network Resilience and Connectivity: Traditional in-band network management fails during outages, making it difficult to troubleshoot and recover remotely. Without complete separation of the management network from production networks, organizations are unable to achieve true resilience from errors, outages, and breaches.
  • Integration Challenges: Implementing AI-driven automation, OOB management, and cybersecurity protections requires seamless interoperability between different vendors’ solutions.
  • Security Concerns: A fully automated data center must have robust access controls, zero-trust security frameworks, and remote threat mitigation capabilities.
  • Skill Gaps: The shift to automation necessitates retraining IT staff, who may be unfamiliar with the latest technologies required to maintain a hands-off data center.

Direct remote access is risky

Image: The traditional management approach relies on production assets. This makes it impossible to achieve resilience, because production failures cut off remote admin access.

How ZPE Systems is Powering Lights-Out Operations

ZPE Systems is already helping companies overcome these challenges and transition to lights-out data center operations. As part of Legrand, ZPE is a key component in a total solution offering that includes everything from cabinets and containment to power distribution and remote access. By leveraging out-of-band management, intelligent automation, and zero-trust security, ZPE enables enterprises to manage their infrastructure remotely and securely.

Isolated Management Infrastructure is critical to lights-out data center operations.

Image: ZPE Systems’ Nodegrid creates an Isolated Management Infrastructure. This gives admins secure remote access, even when the production network fails or suffers an attack.

Key benefits of this management infrastructure include:

  • Reliable Remote Access: ZPE’s OOB solutions ensure secure access to critical infrastructure even when primary networks fail. This is made possible by ZPE’s Isolated Management Infrastructure (IMI), which creates a fully separate management network. This single-box solution helps organizations achieve lights-out operations without device sprawl.
  • Automated Remediation: ZPE’s platform hosts third party applications, Docker containers, and AI and automation solutions. Organizations can leverage data about device health, telemetry, environmentals, and in-band performance, to resolve issues fast and prevent downtime.
  • Hardened Security: ZPE’s solutions are built with security in mind, from local MFA, to self-encrypted disk and signed OS. ZPE also has the most security certifications and validations, including SOC2 Type 2, FIPS 140-3, and ISO27001. Read our full supply chain security assurance pdf.
  • Multi-Vendor Integration: ZPE is the only drop-in solution that works across diverse environments, regardless of which vendor solutions are already in place. This makes it easy to deploy IMI and the resilience architecture necessary for achieving lights-out operations.
  • Comprehensive Data Center Solutions: With Legrand’s full suite of data center infrastructure, organizations benefit from a fully integrated approach that ensures efficiency, scalability, and resilience.

Lights-out data centers are an achievable reality. By addressing the key challenges and leveraging advanced remote management solutions, enterprises can reduce operational costs, enhance security, and improve efficiency. As part of Legrand, ZPE Systems continues to lead the charge in enabling this transformation for organizations across the globe.

See How Vapor IO Achieved Lights-Out Operations with ZPE Systems

Vapor IO is re-architecting the internet. They deploy micro data centers at the network edge, serving markets across the U.S. and Europe. When they needed to achieve true lights-out operations, they chose ZPE Systems’ Nodegrid. Find out how this solution reduced deployment times to just one hour and delivered additional time and cost savings. Download the full case study below.

Get in Touch for a Demo of Lights-Out Data Center Operations

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Why Out-of-Band Management Is Critical to AI Infrastructure

Out-of-Band Management for AI

Artificial intelligence is transforming every corner of industry. Machine learning algorithms are optimizing global logistics, while generative AI tools like ChatGPT are reshaping everyday work and communications. Organizations are rapidly adopting AI, with the global AI market expected to reach $826 billion by 2030, according to Statista. While this growth is reshaping operations and outcomes for organizations in every industry, it brings significant challenges for managing the infrastructure that supports AI workloads.

The Rapid Growth of AI Adoption

AI is no longer a technology that lives only in science fiction. It’s real, and it has quickly become crucial to business strategy and the overall direction of many industries. Gartner reports that 70% of enterprise executives are actively exploring generative AI for their organizations, and McKinsey highlights that 72% of companies have already adopted AI in at least one business function.

It’s easy to understand why organizations are rapidly adopting AI. Here are a few examples of how AI is transforming industries:

  • Healthcare: AI-driven diagnostic tools have improved disease detection rates by up to 30x, while drug discovery timelines are being slashed from years to months.
  • Retail: E-commerce platforms use AI to power personalized recommendations, leading to a revenue increase of 5-25%.
  • Manufacturing: AI in predictive maintenance can help increase productivity by 25%, lower maintenance costs by 25%, and reduce machine downtime by 70%.

AI is a powerful tool that can bring profound outcomes wherever it’s used. But it requires a sophisticated infrastructure of power distribution, cooling systems, computing, GPUs, servers, and networking gear, and the challenge lies in managing this infrastructure.

Infrastructure Challenges Unique to AI

AI environments are complex, with workloads that are both resource-intensive and latency-sensitive. This means organizations face several challenges that are unique to AI:

 

  1. Skyrocketing Energy Demands: AI racks consume between 40kW and 200kW of power, which is 10x more than traditional IT equipment. Energy efficiency in the AI data center is a top priority, especially as data centers account for 1% of global electricity consumption.
  2. Cost of Downtime: AI systems are especially vulnerable to interruptions, which can cause a ripple effect and lead to high costs. A single server failure can disrupt entire model training processes, costing enterprises $9,000 per minute in downtime, as estimated by Uptime Institute.
  3. Cybersecurity Risks: AI processes sensitive data, making AI data centers prime targets for attack. Sophos reports that in 2024, 59% of organizations suffered a ransomware attack, and the average cost to recover (excluding ransom payment) was $2.73 million.
  4. Operational Complexity: AI environments rely on a diverse set of hardware and software systems. Monitoring and managing these components effectively requires real-time visibility into thermal conditions, humidity, particulates, and other environmental and device-related factors.

The Role of Out-of-Band Management in AI

Out-of-band (OOB) management is a must-have for organizations scaling their AI capabilities. Unlike traditional in-band systems that rely on the production network, OOB operates independently to give teams uninterrupted access and control. They can remotely perform monitoring and maintenance tasks to AI infrastructure, troubleshooting, and complete system recovery even if the production network goes offline.

 

How OOB Management Solves Key Challenges:

  • Minimized Downtime: With OOB, IT teams can drastically reduce downtime by troubleshooting issues remotely rather than dispatching teams on-site.
  • Energy Efficiency: Real-time monitoring and optimization of power distribution enable organizations to eliminate zombie servers and other inefficiencies.
  • Enhanced Security: OOB systems isolate management traffic from production networks per CISA’s best practice recommendations, which reduces the attack surface and mitigates cybersecurity risks.
  • Operational Efficiency: Remote monitoring via OOB offers a complete view of environmental conditions and device health, so teams can operate proactively and prevent issues before failures happen.

Use Cases: Out-of-Band Management for AI

There’s no shortage of use cases for AI, but organizations often overlook implementing out-of-band in their environment. Aside from using OOB in AI data centers, here are some real-world use cases of out-of-band management for AI.

1. Autonomous Vehicle R&D

Developers of self-driving technology find it difficult to manage their high-density AI clusters, especially because outages delay testing and development. By implementing OOB management, these developers can reduce recovery times from hours to minutes and shorten development timelines.

2. Financial Services Firms

Banks deploy AI to detect and combat fraud, but these power-hungry systems often lead to inefficient energy usage in the data center. With OOB management, they can gain transparency into GPU and CPU utilization. Not only can they eliminate energy waste, but they can optimize resources to improve model processing speeds.

3. University AI Labs

Universities run AI research on supercomputers, but this strains the underlying infrastructure with high temperatures that can cause failures. OOB management can provide real-time visibility into air temperature, device fan speed, and cooling systems to prevent infrastructure failures.

Download Our Guide, Solving AI Infrastructure Challenges with Out-of-Band Management

Out-of-band management is the key to having reliable, high-performing AI infrastructure. But what does it look like? What devices does it work with? How do you implement it?

Download our whitepaper Solving AI Infrastructure Challenges with Out-of-Band Management for answers. You’ll also get Nvidia’s SuperPOD reference design along with a list of devices that integrate with out-of-band. Click the button for your instant download.

Data Center Environmental Sensors: Everything You Need to Know

According to a recent Uptime Institute survey, severe outages can cost more than $1 million USD and lead to reputational loss as well as business and customer disruption. Humidity, air particulates, and other problems could shorten the lifetime of critical equipment or cause outages. Unfortunately, much of a business’s critical digital infrastructure and services are housed in remote data centers, making it difficult for busy IT teams to keep eyes on the environmental conditions.

Data center environmental sensors can help teams prevent downtime by monitoring conditions in remote infrastructure deployments and alerting administrators to any problems before they lead to equipment failure. This blog explains how environmental sensors work and describes the ideal environmental monitoring solution for minimizing outages.

How data center environmental sensors reduce downtime

Data center environmental sensors are deployed around the rack, cabinet, or cage to collect information about various conditions that could negatively affect equipment like routers, servers, and switches. 

Mitigating environmental risks with data center environmental sensors

Environmental Risk Description How Environmental Sensors Help
Temperature All data center equipment has an optimal operating temperature range, as well as a max temp threshold above which devices may overheat. Environmental sensors monitor ambient temperatures and trigger automated alerts when it gets too hot or too cold in the data center.
Humidity If the air in the data center gets too humid, moisture may collect on the internal components of devices and cause corrosion, shorts, or other failures. Environmental sensors monitor the relative humidity in the DC and alert administrators when there’s a danger of moisture accumulation.
Fire A fire in the data center could burn equipment, raise the ambient temperature beyond acceptable limits, or activate automatic fire suppression controls that damage devices. Environmental sensors detect the heat and smoke from fires, giving DC teams time to shut down systems before they’re damaged.
Tampering A malicious actor who’s able to get past data center security (such as an inside threat) could potentially tamper with equipment to damage or breach it. Tamper detection sensors alert remote teams when data center cabinet doors are opened or a device is physically moved.
Air Particulates Smoke, ozone, and other air particulates could potentially damage data center infrastructure by oxidizing components or clogging vents. Environmental sensors monitor air quality and automatically alert teams when particulates are detected.

These sensors report back to monitoring software that’s either deployed on-premises in the data center or hosted in the cloud. Administrators use this software to view real-time conditions or to configure automated alerts.

Environmental monitoring sensors help reduce outages by giving remote IT teams advance warning that something is wrong with conditions in the data center, enabling them to potentially fix the problem before any systems go down. However, traditional monitoring solutions suffer from a number of limitations.

  1. They need a stable internet connection to allow remote access, so if there’s an ISP outage or unknown failure, teams lose their ability to monitor the situation.
  2. Many of them use on-premises software that requires administrators to connect via VPN to monitor or manage the solution, creating security risks and management hurdles.
  3. Most environmental monitoring systems don’t easily integrate with other remote management tools, leaving administrators with a disjointed patchwork of platforms to wrestle with.

The ideal data center environmental monitoring solution

The Nodegrid data center environmental monitoring platform overcomes these challenges with a combination of out-of-band management, cloud-based software, and a vendor-agnostic architecture.

Nodegrid environmental sensors work with Nodegrid serial consoles to provide remote teams with a virtual presence in the data center. These devices create an instant out-of-band network that uses a dedicated internet connection to provide continuous remote access to all connected sensors and infrastructure. This network doesn’t rely on the primary ISP or production network resources, giving administrators a lifeline to monitor and recover remote data center devices during an outage. The addition of Nodegrid Data Lake also allows teams to collect environmental monitoring data, discover trends and insights, and create better automation to address issues.

Nodegrid’s data center environmental monitoring and infrastructure management software is available on-premises or in the cloud, allowing teams to access critical equipment and respond to alerts from anywhere in the world. Plus, all Nodegrid hardware and software is vendor-neutral, supporting seamless integrations with third-party tools for automation, security, and more.

Schedule a free Nodegrid demo to see our data center environmental sensors and vendor-neutral management platform in action!

Edge Computing Use Cases in Banking

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 Data Center Infrastructure

ZPE Systems – AI Data Center Infrastructure
Artificial intelligence is transforming business operations across nearly every industry, with the recent McKinsey global survey finding that 72% of organizations had adopted AI, and 65% regularly use generative AI (GenAI) tools specifically. GenAI and other artificial intelligence technologies are extremely resource-intensive, requiring more computational power, data storage, and energy than traditional workloads. AI data center infrastructure also requires high-speed, low-latency networking connections and unified, scalable management hardware to ensure maximum performance and availability. This post describes the key components of AI data center infrastructure before providing advice for overcoming common pitfalls to improve the efficiency of AI deployments.

AI data center infrastructure components

A diagram of AI data center infrastructure.

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

Request a demo to learn how Nodegrid can improve the efficiency and resilience of your AI data center infrastructure.
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AI Orchestration: Solving Challenges to Improve AI Value

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