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.

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

Let’s trace the timeline of the attack, how it likely started, and the best practice architecture that could have mitigated or prevented the American Water cyberattack.

Timeline of the October 2024 American Water Cyberattack

• Initial Intrusion (October 5, 2024)
  The attack on American Water was first detected in early October, when cybersecurity monitoring tools flagged suspicious activity within the company’s IT systems. Employees reported an unusual system slowdown, and automated alerts indicated possible unauthorized access.

• Rapid Escalation (October 6-7, 2024)
  Within 24 hours of detection, the attackers had moved deeper into the company’s IT environment. In response, American Water initiated emergency protocols, including isolating key systems to prevent further damage. To contain the breach, critical operational technology (OT) systems — responsible for managing water treatment and distribution — were temporarily shut down

• Public Notification and Response (October 8, 2024)
  American Water notified federal authorities, including the Cybersecurity and Infrastructure Security Agency (CISA), state regulators, and the public. The company reassured customers that water quality had not been compromised, but certain automated operations had been affected, leading to temporary disruptions in water distribution.

• Ongoing Recovery (October 2024 – Present)
  As the investigation continued, third-party cybersecurity firms were brought in to assess the extent of the breach and assist in recovery. Manual operations were implemented in areas where automated systems were impacted. While the threat was contained, the company faced a lengthy process of system restoration and reconfiguration.

Network Virtualization Platforms: Benefits & Best Practices

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:

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.

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.

Schedule a Demo

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.

Watch a demo

AI data center infrastructure components

Computing

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

Storage

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

Networking

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

Power

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

Management

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

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

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

AI data center challenges

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

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

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

Improving operational efficiency with a vendor-neutral platform

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

Make Nodegrid your AI data center orchestration platform

Request a demo to learn how Nodegrid can improve the efficiency and resilience of your AI data center infrastructure.
 Contact Us

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.
   .
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.
   .
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