Edge computing delivers data processing and analysis capabilities to the network’s “edge,” at remote sites like branch offices, warehouses, retail stores, and manufacturing plants. It involves deploying computing resources and lightweight applications very near the devices that generate data, reducing the distance and number of network hops between them. In doing so, edge computing reduces latency and bandwidth costs while mitigating risk, enhancing edge resilience, and enabling real-time insights. This blog discusses the five biggest benefits of edge computing, providing examples and additional resources for companies beginning their edge journey.
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5 benefits of edge computing​

1. Reduces latency

Edge computing leverages data on the same local network as the devices that generate it, cutting down on edge data transmissions over the WAN or Internet. Reducing the number of network hops between devices and applications significantly decreases latency, improving the speed and performance of business intelligence apps, AIOps, equipment health analytics, and other solutions that use edge data.

Some edge applications run on the devices themselves, completely eliminating network hops and facilitating real-time, lag-free analysis. For example, an AI-powered surveillance application installed on an IoT security camera at a walk-up ATM can analyze video feeds in real-time and alert security personnel to suspicious activity as it occurs.​

 

2. Mitigates risk

Edge computing mitigates security and compliance risks by distributing an organization’s sensitive data and reducing off-site transmission. Large, centralized data stores in the cloud or data center are prime targets for cybercriminals because the sheer volume of data involved increases the chances of finding something valuable. Decentralizing data in much smaller edge storage solutions makes it harder for hackers to find the most sensitive information and also limits how much data they can access at one time.

Keeping data at the edge also reduces the chances of interception in transit to cloud or data center storage. Plus, unlike in the cloud, an organization maintains complete control over who and what has access to sensitive data, aiding in compliance with regulations like the GDPR and PCI DSS 4.0.
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3. Lowers bandwidth costs

Many organizations use MPLS (multi-protocol label switching) links to securely connect edge sites to the enterprise network. MPLS bandwidth is much more expensive than regular Internet lines, which makes transmitting edge data to centralized data processing applications extremely costly. Plus, it can take months to provision MPLS at a new site, delaying launches and driving up overhead expenses.

Edge computing significantly reduces MPLS bandwidth utilization by running data-hungry applications on the local network, reserving the WAN for other essential traffic. Combining edge computing with SD-WAN (software-defined wide area networking) and SASE (secure access service edge) technologies can markedly decrease the reliance on MPLS links, allowing organizations to accelerate branch openings and see faster edge ROIs.
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4. Enhances edge resilience

Since edge computing applications run on the same LAN as the devices generating data, they can continue to function even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event. This also allows uninterrupted edge operations in locations with inconsistent (or no) Internet coverage, like offshore oil rigs, agricultural sites, and health clinics in isolated rural communities. Edge computing ensures that organizations don’t miss any vital health or safety alerts and facilitates technological innovation using AI and other data analytics tools in challenging environments..
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5. Enables real-time insights

Sending data from the edge to a cloud or on-premises data lake for processing, transformation, and ingestion by analytics or AI/ML tools takes time, preventing companies from acting on insights at the moment when they’re most useful. Edge computing applications start using data as soon as it’s generated, so organizations can extract value from it right away. For example, a retail store can use edge computing to gain actionable insights on purchasing activity and customer behavior in real-time, so they can move in-demand products to aisle endcaps or staff extra cashiers as needed.
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Simplify your edge computing deployment with Nodegrid

The best way to achieve the benefits of edge computing described above without increasing management complexity or hardware overhead is to use consolidated, vendor-neutral solutions to host, connect, and secure edge workloads. For example, the Nodegrid Gate SR from ZPE Systems delivers an entire stack of edge networking and infrastructure management technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and containers for third-party applications, with an Nvidia Jetson Nano card capable of running AI workloads alongside non-AI data analytics for ultimate efficiency.

Improve your edge computing deployment with Nodegrid

Nodegrid consolidates edge computing deployments to improve operational efficiency without sacrificing performance or functionality. Schedule a free demo to see Nodegrid in action.

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The current cyber threat landscape is daunting, with attacks occurring so frequently that security experts recommend operating under the assumption that your network is already breached. Major cyber attacks – and the disruptions they cause – frequently make news headlines. For example, the recent LendingTree breach exposed consumer data, which could affect the company’s reputation and compliance status. An attack on auto dealership software company CDK Global took down the platform and disrupted business for approximately 15,000 car sellers – an outage that’s still ongoing as of this article’s writing.

The zero trust security methodology outlines the best practices for limiting the blast radius of a successful breach by preventing malicious actors from moving laterally through the network and accessing the most valuable or sensitive resources. Many organizations have already begun their zero trust journey by implementing role-based access controls (RBAC), multi-factor authentication (MFA), and other security solutions, but still struggle with coverage gaps that result in ransomware attacks and other disruptive breaches. This blog provides advice for improving your zero trust security posture with a multi-layered strategy that mitigates weaknesses for complete coverage.

How to improve your zero trust security posture

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Gain a full understanding of your protect surface

Many security strategies focus on defending the network’s “attack surface,” or all the potential vulnerabilities an attacker could exploit to breach the network. However, zero trust is all about defending the “protect surface,” or all the data, assets, applications, and services that an attacker could potentially try to access. The key difference is that zero trust doesn’t ask you to try to cover any possible weakness in a network, which is essentially impossible. Instead, it wants you to look at the resources themselves to determine what has the most value to an attacker, and then implement security controls that are tailored accordingly.

Gaining a full understanding of all the resources on your network can be extraordinarily challenging, especially with the proliferation of SaaS apps, mobile devices, and remote workforces. There are automated tools that can help IT teams discover all the data, apps, and devices on the network. Application discovery and dependency mapping (ADDM) tools help identify all on-premises software and third-party dependencies; cloud application discovery tools do the same for cloud-hosted apps by monitoring network traffic to cloud domains. Sensitive data discovery tools scan all known on-premises or cloud-based resources for personally identifiable information (PII) and other confidential data, and there are various device management solutions to detect network-connected hardware, including IoT devices.
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Micro-segment your network with micro-perimeters

Micro-segmentation is a cornerstone of zero-trust networks. It involves logically separating all the data, applications, assets, and services according to attack value, access needs, and interdependencies. Then, teams implement granular security policies and controls tailored to the needs of each segment, establishing what are known as micro-perimeters. Rather than trying to account for every potential vulnerability with one large security perimeter, teams can just focus on the tools and policies needed to cover the specific vulnerabilities of a particular micro-segment.

Network micro-perimeters help improve your zero trust security posture with:

• Granular access policies granting the least amount of privileges needed for any given workflow. Limiting the number of accounts with access to any given resource, and limiting the number of privileges granted to any given account, significantly reduces the amount of damage a compromised account (or malicious actor) is capable of inflicting.

• Targeted security controls addressing the specific risks and vulnerabilities of the resources in a micro-segment. For example, financial systems need stronger encryption, strict data governance monitoring, and multiple methods of trust verification, whereas an IoT lighting system requires simple monitoring and patch management, so the security controls for these micro-segments should be different.

• Trust verification using context-aware policies to catch accounts exhibiting suspicious behavior and prevent them from accessing sensitive resources. If a malicious outsider compromises an authorized user account and MFA device – or a disgruntled employee uses their network privileges to harm the company – it can be nearly impossible to prevent data exposure. Context-aware policies can stop a user from accessing confidential resources outside of typical operating hours, or from unfamiliar IP addresses, for example. Additionally, user entity and behavior analytics (UEBA) solutions use machine learning to detect other abnormal and risky behaviors that could indicate malicious intent.

Isolate and defend your management infrastructure

For zero trust to be effective, organizations must apply consistently strict security policies and controls to every component of their network architecture, including the management interfaces used to control infrastructure. Otherwise, a malicious actor could use a compromised sysadmin account to hijack the control plane and bring down the entire network.

According to a recent CISA directive, the best practice is to isolate the network’s control plane so that management interfaces are inaccessible from the production network. Many new cybersecurity regulations, including PCI DSS 4.0, DORA, NIS2, and the CER Directive, also either strongly recommend or require management infrastructure isolation.

Isolated management infrastructure (IMI) prevents compromised accounts, ransomware, and other threats from moving laterally to or from the production LAN. It gives teams a safe environment to recover from ransomware or other cyberattacks without risking reinfection, which is known as an isolated recovery environment (IRE). Management interfaces and the IRE should also be protected by granular, role-based access policies, multi-factor authentication, and strong hardware roots of trust to further mitigate risk.

The easiest and most secure way to implement IMI is with Gen 3 out-of-band (OOB) serial console servers, like the Nodegrid solution from ZPE Systems. These devices use alternative network interfaces like 5G/4G LTE cellular to ensure complete isolation and 24/7 management access even during outages. They’re protected by hardware security features like TPM 2.0 and GPS geofencing, and they integrate with zero trust solutions like identity and access management (IAM) and UEBA to enable consistent policy enforcement.

Defend your cloud resources

The vast majority of companies host some or all of their workflows in the cloud, which significantly expands and complicates the attack surface while making it more challenging to identify and defend the protect surface. Some organizations also lack a complete understanding of the shared responsibility model for varying cloud services, increasing the chances of coverage gaps. Additionally, many orgs struggle with “shadow IT,” which occurs when individual business units implement cloud applications without going through onboarding, preventing security teams from applying zero trust controls.

The first step toward improving your zero trust security posture in the cloud is to ensure you understand where your cloud service provider’s responsibilities end and yours begin. For instance, most SaaS providers handle all aspects of security except IAM and data protection, whereas IaaS (Infrastructure-as-a-Service) providers are only responsible for protecting their physical and virtual infrastructure.

It’s also vital that security teams have a complete picture of all the cloud services in use by the organization and a way to deploy and enforce zero trust policies in the cloud. For example, a cloud access security broker (CASB) is a solution that discovers all the cloud services in use by an organization and allows teams to monitor and manage security for the entire cloud architecture. A CASB provides capabilities like data governance, malware detection, and adaptive access controls, so organizations can protect their cloud resources with the same techniques used in the on-premises environment.
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Extend zero trust to the edge

Modern enterprise networks are highly decentralized, with many business operations taking place at remote branches, Internet of Things (IoT) deployment sites, and end-users’ homes. Extending security controls to the edge with on-premises zero trust solutions is very difficult without backhauling all remote traffic through a centralized firewall, which creates bottlenecks that affect performance and reliability. Luckily, the market for edge security solutions is rapidly growing and evolving to help organizations overcome these challenges. 

Security Access Service Edge (SASE) is a type of security platform that delivers core capabilities as a managed, typically cloud-based service for the edge. SASE uses software-defined wide area networking (SD-WAN) to intelligently and securely route edge traffic through the SASE tech stack, allowing the application and enforcement of zero trust controls. In addition to CASB and next-generation firewall (NGFW) features, SASE usually includes zero trust network access (ZTNA), which offers VPN-like functionality to connect remote users to enterprise resources from outside the network. ZTNA is more secure than a VPN because it only grants access to one app at a time, requiring separate authorization requests and trust verification attempts to move to different resources. 

Accelerating the zero trust journey

Zero trust is not a single security solution that you can implement once and forget about – it requires constant analysis of your security posture to identify and defend weaknesses as they arise. The best way to ensure adaptability is by using vendor-agnostic platforms to host and orchestrate zero trust security. This will allow you to add and change security services as needed without worrying about interoperability issues.

For example, the Nodegrid platform from ZPE Systems includes vendor-neutral serial consoles and integrated branch services routers that can host third-party software such as SASE and NGFWs. These devices also provide Gen 3 out-of-band management for infrastructure isolation and network resilience. Nodegrid protects management interfaces with strong hardware roots-of-trust, embedded firewalls, SAML 2.0 integrations, and other zero trust security features. Plus, with Nodegrid’s cloud-based or on-premises management platform, teams can orchestrate networking, infrastructure, and security workflows across the entire enterprise architecture.

Improve your zero trust security posture with Nodegrid

Using Nodegrid as the foundation for your zero trust network infrastructure ensures maximum agility while reducing management complexity. Watch a Nodegrid demo to learn more.

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The continuing trend of enterprise network decentralization to support Internet of Things (IoT) deployments, automation, and edge computing is resulting in rapid growth for the edge security market. Recent research predicts it will reach $82.4 billion by 2031 at a compound annual growth rate (CAGR) of 19.7% from 2024.

Edge security solutions decentralize the enterprise security stack, delivering key firewall capabilities to the network’s edges. This prevents companies from funneling all edge traffic through a centralized data center firewall, reducing latency and improving overall performance.

This guide compares the most popular edge security solutions and offers recommendations for choosing the right vendor for your use case.

Executive summary

There are six single-vendor SASE solutions offering the best combination of features and capabilities for their targeted use cases.
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The best edge security solution for Gen 3 out-of-band (OOB) management, which is critical for infrastructure isolation, resilience, and operational efficiency, is Nodegrid from ZPE Systems. Nodegrid provides secure hardware and software to host other vendors’ tools on a secure, Gen 3 OOB network. It creates a control plane for edge infrastructure that’s completely isolated from breaches on the production network and consolidates an entire edge networking stack into a single solution. Disclaimer: This comparison was written by a third party in collaboration with ZPE Systems using publicly available information gathered from data sheets, admin guides, and customer reviews on sites like Gartner Peer Insights, as of 6/09/2024. Please email us if you have corrections or edits, or want to review additional attributes, at matrix@zpesystems.com.

What are edge security solutions?

Edge security solutions primarily fall into one (or both) of two categories:

• Security Service Edge (SSE) solutions deliver core security features as a managed service. SSE does not come with any networking capabilities, so companies still need a way to securely route edge traffic through the (often cloud-based) security stack. This usually involves software-defined wide area networking (SD-WAN), which was traditionally a separate service that had to be integrated with the SSE stack.
• Secure Access Service Edge (SASE) solutions package SSE together with SD-WAN, preventing companies from needing to deploy and manage multiple vendor solutions.

All the top SSE providers now offer fully integrated SASE solutions with SD-WAN. SASE’s main tech stack is in the cloud, but organizations must install SD-WAN appliances at each branch or edge data center. SASE also typically uses software agents deployed at each site and, in some cases, on all edge devices. Some SASE vendors also sell physical appliances, while others only provide software licenses for virtualized SD-WAN solutions. A third category of edge security solutions offers a secure platform to run other vendors’ SD-WAN and SASE software. These solutions also provide an important edge security capability: management network isolation. This feature ensures that ransomware, viruses, and malicious actors can’t jump from compromised IoT devices to the management interfaces used to control vital edge infrastructure.

Comparing edge security solutions

Palo Alto Prisma SASE

Palo Alto Prisma was named a Leader in Gartner’s 2023 SSE Magic Quadrant for its ability to deliver best-in-class security features. Prisma SASE is a cloud-native, AI-powered solution with the industry’s first native Autonomous Digital Experience Management (ADEM) service. Prisma’s ADEM has built-in AIOps for automatic incident detection, diagnosis, and remediation, as well as self-guided remediation to streamline the end-user experience. Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Palo Alto Prisma SASE Capabilities:

• Zero Trust Network Access (ZTNA) 2.0 – Automated app discovery, fine-grained access controls, continuous trust verification, and deep security inspection.
• Cloud Secure Web Gateway (SWG) – Inline visibility and control of web and SaaS traffic.
• Next-Gen Cloud Access Security Broker (CASB) – Inline and API-based security controls and contextual policies.
• Remote Browser Isolation (RBI) – Creates a secure isolation channel between users and remote browsers to prevent web threats from executing on their devices.
• App acceleration – Application-aware routing to improve “first-mile” connection performance.
• Prisma Access Browser – Policy management for edge devices.
• Firewall as a Service (FWaaS) – Advanced threat protection, URL filtering, DNS security, and other next-generation firewall (NGFW) features.
• Prisma SD-WAN – Elastic networks, app-defined fabric, and Zero Trust security.

Zscaler Zero Trust SASE

Zscaler is another 2023 SSE Magic Quadrant Leader offering a robust single-vendor SASE solution based on its Zero Trust ExchangeTM platform. Zscaler SASE uses artificial intelligence to boost its SWG, firewall, and DEM capabilities. It also offers IoT device management and OT privileged access management, allowing companies to secure unmanaged devices and provide secure remote access to industrial automation systems and other operational technology. Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and future roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology deployments.

Zscaler Zero Trust SASE Capabilities:

• Zscaler Internet Access^TM (ZIA) – SWG cyberthreat protection and zero-trust access to SaaS apps and the web.
• Zscaler Private Access^TM (ZPA) – ZTNA connectivity to private apps and OT devices.
• Zscaler Digital Experience^TM (ZDX) –  DEM with Microsoft Copilot AI to streamline incident management.
• Zscaler Data Protection – CASB/DLP secures edge data across platforms.
• IoT device visibility – IoT device, server, and unmanaged user device discovery, monitoring, and management.
• Privileged OT access – Secure access management for third-party vendors and remote user connectivity to OT systems.
• Zero Trust SD-WAN – Works with the Zscaler Zero Trust Exchange platform to secure edge and branch traffic.

Netskope ONE

Netskope is the only 2023 SSE Magic Quadrant Leader to offer a single-vendor SASE targeted to mid-market companies with smaller budgets as well as larger enterprises. The Netskope ONE platform provides a variety of security features tailored to different deployment sizes and requirements, from standard SASE offerings like ZTNA and CASB to more advanced capabilities such as AI-powered threat detection and user and entity behavior analytics (UEBA). Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Netskope ONE Capabilities:

• Next-Gen SWG – Protection for cloud services, applications, websites, and data.
• CASB – Security for both managed and unmanaged cloud applications.
• ZTNA Next –  ZTNA with integrated software-only endpoint SD-WAN.
• Netskope Cloud Firewall (NCF) – Outbound network traffic security across all ports and protocols.
• RBI – Isolation for uncategorized and risky websites.
• SkopeAI – AI-powered threat detection, UEBA, and DLP
• Public Cloud Security – Visibility, control, and compliance for multi-cloud environments.
• Advanced analytics – 360-degree risk analysis.
• Cloud Exchange – Multi-cloud integration tools.
• DLP – Sensitive data discovery, monitoring, and protection.
• Device intelligence – Zero trust device discovery, risk assessment, and management.
• Proactive DEM – End-to-end visibility and real-time insights.
• SaaS security posture management – Continuous monitoring and enforcement of SaaS security settings, policies, and best practices.
• Borderless SD-WAN – Zero trust connectivity for edge, branch, cloud, remote users, and IoT devices.

Cisco

Cisco is one of the only edge security vendors to offer SASE as a managed service for companies with lean IT operations and a lack of edge networking experience. Cisco Secure Connect SASE-as-a-service includes all the usual SSE capabilities, such as ZTNA, SWG, and CASB, as well as native Meraki SD-WAN integration and a generative AI assistant. Cisco also provides traditional SASE by combining Cisco Secure Access SSE – which includes the Cisco Umbrella Secure Internet Gateway (SIG) – with Catalyst SD-WAN. Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Cisco Secure Connect SASE-as-a-Service Capabilities:

• Clientless ZTNA
• Client-based Cisco AnyConnect secure remote access
• SWG
• Cloud-delivered firewall
• DNS-layer security
• CASB
• DLP
• SAML user authentication
• Generative AI assistant
• Network interconnect intelligent routing
• Native Meraki SD-WAN integration
• Unified management

Cisco Secure Access SASE Capabilities

• ZTNA 
• SWG
• CASB
• DLP
• FWaaS
• DNS-layer security
• Malware protection
• RBI
• Catalyst SD-WAN

Forcepoint ONE

Forcepoint ONE is a cloud-native single-vendor SASE solution placing a heavy emphasis on edge and multi-cloud visibility. Forcepoint ONE aggregates live telemetry from all Forcepoint security solutions and provides visualizations, executive summaries, and deep insights to help companies improve their security posture. Forcepoint also offers what they call data-first SASE, focusing on protecting data across edge and cloud environments while enabling seamless access for authorized users from anywhere in the world. Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging.

Forcepoint ONE Capabilities:

• CASB – Access control and data security for over 800,000 cloud apps on managed and unmanaged devices.
• ZTNA – Secure remote access to private web apps.
• SWG – Includes RBI, content disarm & reconstruction (CDR), and a cloud firewall.
• Data Security – A cloud-native DLP to help enforce compliance across clouds, apps, emails, and endpoints.
• Insights – Real-time analysis of live telemetry data from Forcepoint ONE security products.
• FlexEdge SD-WAN – Secure access for branches and remote edge sites.

Fortinet FortiSASE

Fortinet’s FortiSASE platform combines feature-rich, AI-powered NGFW security functionality with SSE, digital experience monitoring, and a secure SD-WAN solution. Fortinet’s SASE offering includes the FortiGate NGFW delivered as a service, providing access to FortiGuard AI-powered security services like antivirus, application control, OT security, and anti-botnet protection. FortiSASE also integrates with the FortiMonitor DEM SaaS platform to help organizations optimize endpoint application performance. FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

Fortinet FortiSASE Capabilities:

• Antivirus – Protection from the latest polymorphic attacks, ransomware, viruses, and other threats.
• DLP – Prevention of intentional and accidental data leaks.
• AntiSpam – Multi-layered spam email filtering.
• Application Control – Policy creation and management for enterprise and cloud-based applications.
• Attack Surface Security – Security Fabric infrastructure assessments based on major security and compliance frameworks.
• CASB – Inline and API-based cloud application security.
• DNS Security – DNS traffic visibility and filtering.
• IPS – Deep packet inspection (DPI) and SSL inspection of network traffic.
• OT Security – IPS for OT systems including ICS and SCADA protocols.
• AI-Based Inline Malware Prevention – Real-time protection against zero-day exploits and sophisticated, novel threats.
• URL Filtering – AI-powered behavior analysis and correlation to block malicious URLs.
• Anti-Botnet and C2 – Prevention of unauthorized communication attempts from compromised remote servers.
• FortiMonitor DEM – SaaS-based digital experience monitoring.
• Secure SD-WAN – On-premises and cloud-based SD-WAN integrated into the same OS as the SSE security solutions.

Edge isolation and security with ZPE Nodegrid

The Nodegrid platform from ZPE Systems is a different type of edge security solution, providing secure hardware and software to host other vendors’ tools on a secure, Gen 3 out-of-band (OOB) management network. Nodegrid integrated branch services routers use alternative network interfaces (including 5G/4G LTE) and serial console technology to create a control plane for edge infrastructure that’s completely isolated from breaches on the production network. It uses hardware security features like secure boot and geofencing to prevent physical tampering, and it supports strong authentication methods and SAML integrations to protect the management network. Nodegrid’s OOB also ensures remote teams have 24/7 access to manage, troubleshoot, and recover edge deployments even during a major network outage or ransomware infection. Plus, Nodegrid’s ability to host Guest OS, including Docker containers and VNFs, allows companies to consolidate an entire edge networking stack in a single platform. Nodegrid devices like the Gate SR with Nvidia Jetson Nano can even run edge computing and AI/ML workloads alongside SASE. .

ZPE Nodegrid Edge Security Capabilities

• Vendor-neutral platform – Hosting for third-party applications and services, including Docker containers and virtualized network functions.
• Gen 3 OOB – Management interface isolation and 24/7 remote access during outages and breaches.
• Branch networking – Routing and switching, VNFs, and software-defined branch networking (SD-Branch).
• Secure boot – Password-protected BIO/Grub and signed software.
• Latest kernel & cryptographic modules – 64-bit OS with current encryption and frequent security patches.
• SSO with SAML, 2FA, & remote authentication – Support for Duo, Okta, Ping, and ADFS.
• Geofencing – GPS tracking with perimeter crossing detection.
• Fine-grain authorization – Role-based access control.
• Firewall – Native IPSec & Fail2Ban intrusion prevention and third-party extensibility.
• Tampering protection – Configuration checksum and change detection with a configuration ‘reset’ button.
• TPM encrypted storage – Software encryption for SSD hardware storage.

Deploy edge security solutions on the vendor-neutral Nodegrid OOB platform

Nodegrid’s secure hardware and vendor-neutral OS make it the perfect platform for hosting other vendors’ SSE, SD-WAN, and SASE solutions. Reach out today to schedule a free demo.

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The edge computing market is growing fast, with experts predicting edge computing spending to reach almost $350 billion in 2027. Companies use edge computing to leverage data from Internet of Things (IoT) sensors and other devices at the periphery of the network in real-time, unlocking faster insights, accelerating ROIs for artificial intelligence and machine learning investments, and much more. This blog highlights 7 edge computing examples from across many different industries and provides tips and best practices for each use case.

What is edge computing?

Edge computing involves moving compute capabilities – processing units, RAM, storage, data analysis software, etc. – to the network’s edges. This allows companies to analyze or otherwise use edge data in real-time, without transmitting it to a central data center or the cloud.

Edge Computing Learning Center

Edge Computing vs On-Premises: A Comparison Edge Computing vs Cloud Computing Edge Computing Requirements Gartner Market Guide for Edge Computing

Edge computing shortens the physical and logical distance between data-generating devices and the applications that use that data, which reduces bandwidth costs and network latency while simplifying many aspects of data security and compliance.

7 Edge computing examples

Below are 7 examples of how organizations use edge computing, along with best practices for overcoming the typical challenges involved in each use case. Click the links in the table for more information about each example.

Examples	Best Practices
Monitoring inaccessible equipment in the oil & gas industry	Use a vendor-neutral edge computing & networking platform to reduce the tech stack at each site.
Remotely managing and securing automated Smart buildings	Isolate the management interfaces for automated building management systems from production to reduce risk.
Analyzing patient health data generated by mobile devices	Protect patient privacy with strong hardware roots-of-trust, Zero Trust Edge integrations, and control plane/data plane separation.
Reducing latency for live streaming events and online gaming	Use all-in-one, vendor-neutral devices to minimize hardware overhead and enable cost-effective scaling.
Improving performance and business outcomes for AI/ML	Streamline operations by using a vendor-neutral platform to remotely monitor and orchestrate edge AI/ML deployments.
Enhancing remote surveillance capabilities at banks and ATMs	Isolate the management interfaces for all surveillance systems using Gen 3 OOB to prevent compromise.
Extending data analysis to agriculture sites with limited Internet access	Deploy edge gateway routers with environmental sensors to monitor operating conditions and prevent equipment failures.

1. Monitoring and managing inaccessible equipment in the oil and gas industry

The oil and gas industry uses IoT sensors to monitor flow rates, detect leaks, and gather other critical information about human-inaccessible equipment and operations. With drilling rigs located offshore and in extremely remote locations, ensuring reliable internet access to communicate with cloud-based or on-premises monitoring applications can be tricky. Dispatching IT teams to diagnose and repair issues is also costly, time-consuming, and risky. Edge computing allows oil and gas companies to process data on-site and in real-time, so safety issues and potential equipment failures are caught and remediated as soon as possible, even when Internet access is spotty.

2. Remotely managing and securing fully automated Smart buildings

Smart buildings use IoT sensors to monitor and control building functions such as HVAC, lighting, power, and security. Property management companies and facilities departments use data analysis software to automatically determine optimal conditions, respond to issues, and alert technicians when emergencies occur. Edge computing allows these automated processes to respond to changing conditions in real-time, reducing the need for on-site personnel and improving operational efficiency.

3. Analyzing patient health data generated by mobile devices in the healthcare industry

Healthcare organizations use data analysis software, including AI and machine learning, to analyze patient health data generated by insulin pumps, pacemakers, imaging devices, and other IoT medical technology. Keeping that data secure is critical for regulatory compliance, so it must be funneled through a firewall on its way to cloud-based or data center applications, increasing latency and preventing real-time response to potentially life-threatening health issues. Edge computing for healthcare moves patient monitoring and data analysis applications to the same local network (or even the same onboard chip) as the sensors generating most of the data, reducing security risks and latency. Some edge computing applications for healthcare can operate without a network connection most of the time, using built-in cellular interfaces and ATT FirstNet connections to send emergency alerts as needed without exposing any private patient data.

4. Reducing latency for live streaming events and online gaming

Streaming live content requires low-latency processing for every user regardless of their geographic location, which is hard to deliver from a few large, strategically placed data centers. Edge computing decentralizes computing resources, using relatively small deployments in many different locations to bring services closer to audience members and gamers. Edge computing reduces latency for streaming sports games, concerts, and other live events, as well as online multiplayer games where real-time responses are critical to the customer experience.

5. Improving performance and business outcomes for artificial intelligence/machine learning

Artificial intelligence and machine learning applications provide enhanced data analysis capabilities for essentially any use case, but they must ingest vast amounts of data to do so. Securely transmitting and storing edge and IoT data and preparing it for ingestion in data lakes or data warehouses located in the cloud or data center takes significant time and effort, which may prevent companies from getting the most out of their AI investment. Edge computing for AI/ML eliminates transmission and storage concerns by processing data directly from the sources. Edge computing lets companies leverage their edge data for AI/ML much faster, enabling near-real-time insights, improving application performance, and providing accelerated business value from AI investments.

6. Enhancing remote surveillance capabilities at banks and ATMs

Constantly monitoring video surveillance feeds from banks and ATMs is very tedious for people, but machines excel at it. AI-powered video surveillance systems use advanced machine-learning algorithms to analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security teams. With edge computing, these solutions can analyze surveillance data in real-time, so they could potentially catch a crime as it’s occurring. Edge computing also keeps surveillance data on-site, reducing bandwidth costs, network latency, and the risk of interception.

7. Extending data analysis to agriculture sites with limited Internet access

The agricultural sector uses IoT technology to monitor growing conditions, equipment performance, crop yield, and much more. Many of these devices use cellular connections to transmit data to the cloud for analysis which, as we’ve already discussed ad nauseam, introduces latency, increases bandwidth costs, and creates security risks. Edge computing moves this data processing on-site to reduce delays in critical applications like livestock monitoring and irrigation control. It also allows farms to process data on a local network, reducing their reliance on cellular networks that aren’t always reliable in remote and rural areas.

Edge computing for any use case

The potential uses for edge computing are nearly limitless. A shift toward distributed, real-time data analysis allows companies in any industry to get faster insights, reduce inefficiencies, and see more value from AI initiatives.

Simplify your edge deployment with Nodegrid

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Both edge computing and cloud computing involve moving computational resources – such as CPUs (central processing units), GPUs (graphics processing units), RAM (random access memory), and data storage – out of the centralized, on-premises data center. As such, both represent massive shifts in enterprise network designs and how companies deploy, manage, secure, and use computing resources. Edge and cloud computing also create new opportunities for data processing, which is sorely needed as companies generate more data than ever before, thanks in no small part to an explosion in Internet of Things (IoT) and artificial intelligence (AI) adoption. By 2025, IoT devices alone are predicted to generate 80 zettabytes of data, much of it decentralized around the edges of the network. AI, machine learning, and other data analytics applications, meanwhile, require vast quantities of data (and highly scalable infrastructure) to provide accurate insights. This guide compares edge computing vs cloud computing to help organizations choose the right deployment model for their use case.

Table of Contents

Defining edge computing vs cloud computing Examples of edge computing vs cloud computing The advantages of edge computing over cloud computing The disadvantages of edge computing compared to cloud computing Edge native applications vs cloud native applications Choosing edge computing vs cloud computing

Defining edge computing vs cloud computing

Edge computing involves deploying computing capabilities to the network’s edges to enable on-site data processing for Internet of Things (IoT) sensors, operational technology (OT), automated infrastructure, and other edge devices and services. Edge computing deployments are highly distributed across remote sites far from the network core, such as oil & gas rigs, automated manufacturing plants, and shipping warehouses. Ideally, organizations use a centralized (usually cloud-based) orchestrator to oversee and conduct operations across the distributed edge computing architecture.

Diagram showing an example edge computing architecture controlled by a cloud-based edge orchestrator.

Reducing the number of network hops between edge devices and the applications that process and use edge data enables real-time data processing, reduces MPLS bandwidth costs, improves performance, and keeps private data within the security micro-perimeter. Cloud computing involves using remote computing resources over the Internet to run applications, process and store data, and more. Cloud service providers manage the physical infrastructure and allow companies to easily scale their virtual computing resources with the click of a button, significantly reducing operational costs and complexity over on-premises and edge computing deployments.

Examples of edge computing vs cloud computing

Edge computing works best for workloads requiring real-time data processing using fairly lightweight applications, especially in locations with inconsistent or unreliable Internet access or where privacy/compliance is a major concern. Example edge computing use cases include:

• Orchestrating operational technology (OT) in remote manufacturing plants
• Monitoring safety and production data from sensors inside gas lines
• Analyzing patient data from wearable health monitors to provide real-time emergency alerts

Cloud computing is well-suited to workloads requiring extensive computational resources that can scale on-demand, but that aren’t time-sensitive. Example use cases include:

• Running large artificial intelligence and machine learning solutions
• Centralizing collaboration tools like source code version control
• Orchestrating distributed edge operations from a single platform

The advantages of edge computing over cloud computing

Using cloud-based applications to process edge device data involves transmitting that data from the network’s edges to the cloud provider’s data center, and vice versa. Transmitting data over the open Internet is too risky, so most organizations route the traffic through a security appliance such as a firewall to encrypt and protect the data. Often these security solutions are off-site, in the company’s central data center, or, best-case scenario, a SASE point-of-presence (PoP), adding more network hops between edge devices and the cloud applications that service them.  This process increases bandwidth usage and introduces latency, preventing real-time data processing and negatively affecting performance.

Edge computing moves data processing resources closer to the source, eliminating the need to transmit this data over the Internet. This improves performance by reducing (or even removing) network hops and preventing network bottlenecks at the centralized firewall. Edge computing also lets companies use their valuable edge data in real time, enabling faster insights and greater operational efficiencies.

Edge computing mitigates the risk involved in storing and processing sensitive or highly regulated data in a third-party computing environment, giving companies complete control over their data infrastructure. It can also help reduce bandwidth costs by eliminating the need to route edge data through VPNs or MPLS links to apply security controls.

Edge computing advantages:

• Improves network and application performance
• Enables real-time data processing and insights
• Simplifies security and compliance
• Reduces MPLS bandwidth costs

The disadvantages of edge computing compared to cloud computing

Cloud computing resources are highly scalable, allowing organizations to meet rapidly changing requirements without the hassle of purchasing, installing, and maintaining additional hardware and software licenses. Edge computing still involves physical, on-premises infrastructure, making it far less scalable than the cloud. However, it’s possible to improve edge agility and flexibility by using vendor-neutral platforms to run and manage edge resources. An open platform like Nodegrid allows teams to run multiple edge computing applications from different vendors on the same box, swap out services as business needs evolve, and deploy automation to streamline multi-vendor edge device provisioning from a single orchestrator.

Diagram showing how the Nodegrid Mini SR combines edge computing and networking capabilities on a small, affordable, flexible platform.

Organizations often deploy edge computing in less-than-ideal operating environments, such as closets and other cramped spaces that lack the strict HVAC controls that maintain temperature and humidity in cloud data centers. These environments also typically lack the physical security controls that prevent unauthorized individuals from tampering with equipment, such as guarded entryways, security cameras, and biometric locks. The best way to mitigate this disadvantage is with an environmental monitoring system that uses sensors to detect temperature and humidity changes that could cause equipment failures as well as proximity alarms to notify administrators when someone gets too close. It’s also advisable to use hermetically sealed edge computing devices capable of operating in extreme temperatures and with built-in security features making them tamper-proof.

Cloud computing is often more resilient than edge computing because cloud service providers must maintain a certain level of continuous uptime to meet service level agreements (SLAs). Edge computing operations could be disrupted by network equipment failures, ISP outages, ransomware attacks, and other adverse events, so it’s essential to implement resilience measures that keep services running (if in a degraded state) and allow remote teams to fix problems without having to be on site. Edge resilience measures include Gen 3 out-of-band management, control plane/data plane separation (also known as isolated management infrastructure or IMI), and isolated recovery environments (IRE).

Edge computing disadvantages:

• Less scalable than cloud infrastructure
• Lack of environmental and security controls
• Requires additional resilience measures

Edge-native applications vs cloud-native applications

Edge-native applications and cloud-native applications are similar in that they use containers and microservices architectures, as well as CI/CD (continuous integration/continuous delivery) and other DevOps principles.

Cloud-native applications leverage centralized, scalable resources to perform deep analysis of long-lived data in long-term hot storage environments. Edge-native applications are built to leverage limited resources distributed around the network’s edges to perform real-time analysis of ephemeral data that’s constantly moving. Typically, edge-native applications are highly contextualized for a specific use case, whereas cloud-native applications offer broader, standardized capabilities. Another defining characteristic of edge-native applications is the ability to operate independently when needed while still integrating seamlessly with the cloud, upstream resources, remote management, and centralized orchestration.

Choosing edge computing vs cloud computing

Both edge computing and cloud computing have unique advantages and disadvantages that make them well-suited for different workloads and use cases. Factors like increasing data privacy regulations, newsworthy cloud provider outages, greater reliance on human-free IoT and OT deployments, and an overall trend toward decentralizing business operations are pushing organizations to adopt edge computing. However, most companies still rely heavily on cloud resources and will continue to do so, making it crucial to ensure seamless interoperability between the edge and the cloud.

The best way to ensure integration is by using vendor-neutral platforms. For example, Nodegrid integrated services routers like the Gate SR provide multi-vendor out-of-band serial console management for edge infrastructure and devices, using an embedded Jetson Nano card to support edge computing and AI workloads. The ZPE Cloud management platform unifies orchestration for the entire Nodegrid-connected architecture, delivering 360-degree control over complex and highly distributed networks. Plus, Nodegrid easily integrates – or even directly hosts – other vendors’ solutions for edge data processing, IT automation, SASE, and more, making edge operations more cost-effective. Nodegrid also provides the complete control plane/data plane separation needed to ensure edge resilience.

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Edge computing is rapidly gaining popularity as more  organizations see the benefits of decentralizing data processing for Internet of Things (IoT) deployments, machine learning applications, operational technology (OT), AI and machine learning, and other edge use cases. This guide defines edge computing and edge-native applications, highlights a few key use cases, describes the typical components of an edge deployment, and provides additional resources for building your own edge computing architecture.

Table of Contents

What is edge computing? Edge-native applications Edge computing use cases Edge computing architecture design How to build an edge computing architecture with Nodegrid

What is edge computing?

The Open Glossary of Edge Computing defines it as deploying computing capabilities to the edges of a network to improve performance, reduce operating costs, and increase resilience. Edge computing reduces the number of network hops between data-generating devices and the applications that process and use that data, mitigating latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Image: A diagram showing the migration path from on-premises computing to edge computing, along with the associated level of security risk.

Edge-native applications

Edge-native applications are built from the ground up to harness edge computing’s unique capabilities while mitigating the limitations. They leverage some cloud-native principles, such as containers, microservices, and CI/CD (continuous integration/continuous delivery), with several key differences.

Edge-Native vs. Cloud-Native Applications

	Edge-Native	Cloud-Native
Topology	Distributed	Centralized
Compute	Real-time processing with limited resources	Deep processing with scalable resources
Data	Constantly changing and moving	Long-lived and at rest in a centralized location
Capabilities	Contextualized	Standardized
Location	Anywhere	Cloud data center

Source: Gartner

Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed. Crucially, they allow organizations to actually leverage their edge data in real-time, rather than just collecting it for later processing.

Edge computing use cases

Nearly every industry has potential use cases for edge computing, including:

Industry	Edge Computing Use Cases
Healthcare	Mitigating security, privacy, and HIPAA compliance concerns with local data processing Improving patient health outcomes with real-time alerts that don’t require Internet access Enabling emergency mobile medical intervention while reducing mistakes
Finance	Reducing security and regulatory risks through local computing and edge infrastructure isolation Getting fast, localized business insights to improve revenue and customer service Deploying AI-powered surveillance and security solutions without network bottlenecks
Energy	Enabling network access and real-time data processing for airgapped and isolated environments Improving efficiency with predictive maintenance recommendations and other insights Proactively identifying and remediating safety, quality, and compliance issues
Manufacturing	Getting real-time, data-driven insights to improve manufacturing efficiency and product quality Reducing the risk of confidential production data falling into the wrong hands in transit Ensuring continuous operations during network outages and other adverse events Using AI with computer vision to ensure worker safety and quality control of fabricated components/products
Utilities/Public Services	Using IoT technology to deliver better services, improve public safety, and keep communities connected Reducing the fleet management challenges involved in difficult deployment environments Aiding in disaster recovery and resilience with distributed redundant edge resources

To learn more about the specific benefits and uses of edge computing for each industry, read Distributed Edge Computing Use Cases.

Edge computing architecture design

An edge computing architecture consists of six major components:

Edge Computing Components	Description	Best Practices
Devices generating edge data	IoT devices, sensors, controllers, smartphones, and other devices that generate data at the edge	Use automated patch management to keep devices up-to-date and protect against known vulnerabilities
Edge software applications	Analytics, machine learning, and other software deployed at the edge to use edge data	Look for edge-native applications that easily integrate with other tools to prevent edge sprawl
Edge computing infrastructure	CPUs, GPUs, memory, and storage used to process data and run edge applications	Use vendor-neutral, multi-purpose hardware to reduce overhead and management complexity
Edge network infrastructure and logic	Wired and wireless connectivity, routing, switching, and other network functions	Deploy virtualized network functions and edge computing on common, vendor-neutral hardware
Edge security perimeter	Firewalls, endpoint security, web filtering, and other enterprise security functionality	Implement edge-centric security solutions like SASE and SSE to prevent network bottlenecks while protecting edge data
Centralized management and orchestration	An EMO (edge management and orchestration) platform used to oversee and conduct all edge operations	Use a cloud-based, Gen 3 out-of-band (OOB) management platform to ensure edge resilience and enable end-to-end automation

Click here to learn more about the infrastructure, networking, management, and security components of an edge computing architecture.

How to build an edge computing architecture with Nodegrid

Nodegrid is a Gen 3 out-of-band management platform that streamlines edge computing with vendor-neutral solutions and a centralized, cloud-based orchestrator.

Image: A diagram showing all the edge computing and networking capabilities provided by the Nodegrid Gate SR.

Nodegrid integrated services routers deliver all-in-one edge computing and networking functionality while taking up 1RU or less. A Nodegrid box like the Gate SR provides Ethernet and Serial switching, serial console/jumpbox management, WAN routing, wireless networking, and 5G/4G cellular for network failover or out-of-band management. It includes enough CPU, memory, and encrypted SSD storage to run edge computing workflows, and the x86-64bit Linux-based Nodegrid OS supports virtualized network functions, VMs, and containers for edge-native applications, even those from other vendors. The new Gate SR also comes with an embedded NVIDIA Jetson Orin NanoTM module featuring dual CPUs for EMO of AI workloads and infrastructure isolation.

Nodegrid SRs can also host SASE, SSE, and other security solutions, as well as third-party automation from top vendors like Redhat and Salt. Remote teams use the centralized, vendor-neutral ZPE Cloud platform (an on-premises version is available) to deploy, monitor, and orchestrate the entire edge architecture. Management, automation, and orchestration workflows occur over the Gen 3 OOB control plane, which is separated and isolated from the production network. Nodegrid OOB uses fast, reliable network interfaces like 5G cellular to enable end-to-end automation and ensure 24/7 remote access even during major outages, significantly improving edge resilience.

Streamline your edge deployment

The Nodegrid platform from ZPE Systems reduces the cost and complexity of building an edge computing architecture with vendor-neutral, all-in-one devices and centralized EMO. Request a free Nodegrid demo to learn more.

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The Internet of Things (IoT) and remote work capabilities have allowed many organizations to conduct critical business operations at the enterprise network’s edges. Wearable medical sensors, automated industrial machinery, self-service kiosks, and other edge devices must transmit data to and from software applications, machine learning training systems, and data warehouses in centralized data centers or the cloud. Those transmissions eat up valuable MPLS bandwidth and are attractive targets for cybercriminals.

Edge computing involves moving data processing systems and applications closer to the devices that generate the data at the network’s edges. Edge computing can reduce WAN traffic to save on bandwidth costs and improve latency. It can also reduce the attack surface by keeping edge data on the local network or, in some cases, on the same device.

Running powerful data analytics and artificial intelligence applications outside the data center creates specific challenges. For example, space is usually limited at the edge, and devices might be outdoors where power and climate control are more complex. This guide discusses the edge computing requirements for hardware, networking, availability, security, and visibility to address these concerns.

Edge computing requirements

The primary requirements for edge computing are:

1. Compute

As the name implies, edge computing requires enough computing power to run the applications that process edge data. The three primary concerns are:

• Processing power: CPUs (central processing units), GPUs (graphics processing units), or SoCs (systems on chips)
• Memory: RAM (random access memory)
• Storage: SSDs (solid state drives), SCM (storage class memory), or Flash memory
• Coprocessors: Supplemental processing power needed for specific tasks, such as DPUs (data processing units) for AI

The specific edge computing requirements for each will vary, as it’s essential to match the available compute resources with the needs of the edge applications.

2. Small, ruggedized chassis

Space is often quite limited in edge sites, and devices may not be treated as delicately as they would be in a data center. Edge computing devices must be small enough to squeeze into tight spaces and rugged enough to handle the conditions they’ll be deployed in. For example, smart cities connect public infrastructure and services using IoT and networking devices installed in roadside cabinets, on top of streetlights, and in other challenging deployment sites. Edge computing devices in other applications might be subject to constant vibrations from industrial machinery, the humidity of an offshore oil rig, or even the vacuum of outer space.

3. Power

In some cases, edge deployments can use the same PDUs (power distribution units) and UPSes (uninterruptible power supplies) as a data center deployment. Non-traditional implementations, which might be outdoors, underground, or underwater, may require energy-efficient edge computing devices using alternative power sources like batteries or solar.

4. Wired & wireless connectivity

Edge computing systems must have both wired and wireless network connectivity options because organizations might deploy them somewhere without access to an Ethernet wall jack. Cellular connectivity via 4G/5G adds more flexibility and ideally provides network failover/out-of-band capabilities.

5. Out-of-band (OOB) management

Many edge deployment sites don’t have any IT staff on hand, so teams manage the devices and infrastructure remotely. If something happens to take down the network, such as an equipment failure or ransomware attack, IT is completely cut off and must dispatch a costly and time-consuming truck roll to recover. Out-of-band (OOB) management creates an alternative path to remote systems that doesn’t rely on any production infrastructure, ensuring teams have continuous access to edge computing sites even during outages.

6. Security

Edge computing reduces some security risks but can create new ones. Security teams carefully monitor and control data center solutions, but systems at the edge are often left out. Edge-centric security platforms such as SSE (Security Service Edge) help by applying enterprise Zero Trust policies and controls to edge applications, devices, and users. Edge security solutions often need hardware to host agent-based software, which should be factored into edge computing requirements and budgets. Additionally, edge devices should have secure Roots of Trust (RoTs) that provide cryptographic functions, key management, and other features that harden device security.

7. Visibility

Because of a lack of IT presence at the edge, it’s often difficult to catch problems like high humidity, overheating fans, or physical tampering until they affect the performance or availability of edge computing systems. This leads to a break/fix approach to edge management, where teams spend all their time fixing issues after they occur rather than focusing on improvements and innovations. Teams need visibility into environmental conditions, device health, and security at the edge to fix issues before they cause outages or breaches.

Streamlining edge computing requirements

An edge computing deployment designed around these seven requirements will be more cost-effective while avoiding some of the biggest edge hurdles. Another way to streamline edge deployments is with consolidated, vendor-neutral devices that combine core networking and computing capabilities with the ability to integrate and unify third-party edge solutions. For example, the Nodegrid platform from ZPE Systems delivers computing power, wired & wireless connectivity, OOB management, environmental monitoring, and more in a single, small device. ZPE’s integrated edge routers use the open, Linux-based Nodegrid OS capable of running Guest OSes and Docker containers for your choice of third-party AI/ML, data analytics, SSE, and more. Nodegrid also allows you to extend automated control to the edge with Gen 3 out-of-band management for greater efficiency and resilience.

Want to learn more about how Nodegrid makes edge computing easier and more cost-effective?

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A single hour of downtime costs organizations more than $300,000 in lost business, making network and service reliability critical to revenue. The biggest challenge facing IT infrastructure teams is ensuring network resilience, which is the ability to continue operating and delivering services during equipment failures, ransomware attacks, and other emergencies. This guide discusses IT infrastructure management best practices for creating and maintaining more resilient enterprise networks.
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IT infrastructure management best practices

The following IT infrastructure management best practices help improve network resilience while streamlining operations. Click the links on the left for a more detailed look at the technologies and processes involved with each.

Isolated management infrastructure (IMI)

Management interfaces provide the crucial path to monitoring and controlling critical infrastructure, like servers and switches, as well as crown-jewel digital assets like intellectual property (IP). If management interfaces are exposed to the internet or rely on the production network, attackers can easily hijack your critical infrastructure, access valuable resources, and take down the entire network. This is why CISA released a binding directive that instructs organizations to move management interfaces to a separate network, a practice known as isolated management infrastructure (IMI).

The best practice for building an IMI is to use Gen 3 out-of-band (OOB) serial consoles, which unify the management of all connected devices and ensure continuous remote access via alternative network interfaces (such as 4G/5G cellular). OOB management gives IT teams a lifeline to troubleshoot and recover remote infrastructure during equipment failures and outages on the production network. The key is to ensure that OOB serial consoles are fully isolated from production and can run the applications, tools, and services needed to fight through a ransomware attack or outage without taking critical infrastructure offline for extended periods. This essentially allows you to instantly create a virtual War Room for coordinated recovery efforts to get you back online in a matter of hours instead of days or weeks. An IMI using out-of-band serial consoles also provides a safe environment to recover from ransomware attacks. The pervasive nature of ransomware and its tendency to re-infect cleaned systems mean it can take companies between 1 and 6 months to fully recover from an attack, with costs and revenue losses mounting with every day of downtime. The best practice is to use OOB serial consoles to create an isolated recovery environment (IRE) where teams can restore and rebuild without risking reinfection.
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Network and infrastructure automation

As enterprise network architectures grow more complex to support technologies like microservices applications, edge computing, and artificial intelligence, teams find it increasingly difficult to manually monitor and manage all the moving parts. Complexity increases the risk of configuration mistakes, which cause up to 35% of cybersecurity incidents. Network and infrastructure automation handles many tedious, repetitive tasks prone to human error, improving resilience and giving admins more time to focus on revenue-generating projects.

Additionally, automated device provisioning tools like zero-touch provisioning (ZTP) and configuration management tools like RedHat Ansible make it easier for teams to recover critical infrastructure after a failure or attack. Network and infrastructure automation help organizations reduce the duration of outages and allow small IT infrastructure teams to manage large enterprise networks effectively, improving resilience and reducing costs.

Vendor-neutral platforms

Most enterprise networks bring together devices and solutions from many providers, and they often don’t interoperate easily. This box-based approach creates vendor lock-in and technical debt by preventing admins from using the tools or scripting languages they’re familiar with, and it makes a fragmented, complex architecture of management solutions that are difficult to operate efficiently. Organizations also end up compromising on features, ending up with a lot of stuff they don’t need and too little of what they do need.

A vendor-neutral IT infrastructure management platform allows teams to unify all their workflows and solutions. It integrates your administrators’ favorite tools to reduce technical debt and provides a centralized place to deploy, orchestrate, and monitor the entire network. It also extends technologies like OOB, automation, and AIOps to otherwise unsupported legacy and mixed-vendor solutions. Such a platform is revolutionary in the same way smartphones were – instead of needing a separate calculator, watch, pager, phone, etc., everything was combined in a single device. A vendor-neutral management platform allows you to run all the apps, services, and tools you need without buying a bunch of extra hardware. It’s a crucial IT infrastructure management best practice for resilience because it consolidates and unifies network architectures to reduce complexity and prevent human error.

AIOps

AIOps applies artificial intelligence technologies to IT operations to maximize resilience and efficiency. Some AIOps use cases include:

• Security detection: AIOps security monitoring solutions are better at catching novel attacks (those using methods never encountered or documented before) than traditional, signature-based detection methods that rely on a database of known attack vectors.
• Data analysis: AIOps can analyze all the gigabytes of logs generated by network infrastructure and provide health visualizations and recommendations for preventing potential issues or optimizing performance.
• Root-cause analysis (RCA): Ingesting infrastructure logs allows AIOps to identify problems on the network, perform root-cause analysis to determine the source of the issues, and create & prioritize service incidents to accelerate remediation.

AIOps is often thought of as “intelligent automation” because, while most automation follows a predetermined script or playbook of actions, AIOps can make decisions on-the-fly in response to analyzed data. AIOps and automation work together to reduce management complexity and improve network resilience.

IT infrastructure management best practices for maximum resilience

Network resilience is one of the top IT infrastructure management challenges facing modern enterprises. These IT infrastructure management best practices ensure resilience by isolating management infrastructure from attackers, reducing the risk of human error during configurations and other tedious workflows, breaking vendor lock-in to decrease network complexity, and applying artificial intelligence to the defense and maintenance of critical infrastructure.

Need help getting started with these practices and technologies? ZPE Systems can help simplify IT infrastructure management with the vendor-neutral Nodegrid platform. Nodegrid’s OOB serial consoles and integrated branch routers allow you to build an isolated management infrastructure that supports your choice of third-party solutions for automation, AIOps, and more.

Want to learn how to make IT infrastructure management easier with Nodegrid?

To learn more about implementing IT infrastructure management best practices for resilience with Nodegrid, download our Network Automation Blueprint. 

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Terminal servers are network management devices providing remote access to and control over remote infrastructure. They typically connect to infrastructure devices via serial ports (hence their alternate names, serial consoles, console servers, serial console routers, or serial switches). IT teams use terminal servers to consolidate remote device management and create an out-of-band (OOB) control plane for remote network infrastructure. Terminal servers offer several benefits over other remote management solutions, such as better performance, resilience, and security. This guide answers all your questions about terminal servers, discussing their uses and benefits before describing what to look for in the best terminal server solution.

What is a terminal server?

A terminal server is a networking device used to manage other equipment. It directly connects to servers, switches, routers, and other equipment using management ports, which are typically (but not always) serial ports. Network administrators remotely access the terminal server and use it to manage all connected devices in the data center rack or branch where it’s installed.

What are the uses for terminal servers?

Network teams use terminal servers for two primary functions: remote infrastructure management consolidation and out-of-band management.

1. Terminal servers unify management for all connected devices, so administrators don’t need to log in to each separate solution individually. Terminal servers save significant time and effort, which reduces the risk of fatigue and human error that could take down the network.
2. Terminal servers provide remote out-of-band (OOB) management, creating a separate, isolated network dedicated to infrastructure management and troubleshooting. OOB allows administrators to troubleshoot and recover remote infrastructure during equipment failures, network outages, and ransomware attacks.

Learn more about using OOB terminal servers to recover from ransomware attacks by reading How to Build an Isolated Recovery Environment (IRE).

What are the benefits of terminal servers?

There are other ways to gain remote OOB management access to remote infrastructure, such as using Intel NUC jump boxes. Despite this, terminal servers are the better option for OOB management because they offer benefits including:

The benefits of terminal servers

Examples of terminal servers

Examples of popular terminal server solutions include the Opengear CM8100, the Avocent ACS8000, and the Nodegrid Serial Console Plus. The Opengear and Avocent solutions are second-generation, or Gen 2, terminal servers, which means they provide some automation support but suffer from vendor lock-in. The Nodegrid solution is the only Gen 3 terminal server, offering unlimited integration support for 3rd-party automation, security, SD-WAN, and more.

What to look for in the best terminal server

Terminal servers have evolved, so there is a wide range of options with varying capabilities and features. Some key characteristics of the best terminal server include:

• 5G/4G LTE and Wi-Fi options for out-of-band access and network failover
• Support for legacy devices without costly adapters or complicated configuration tweaks
• Advanced authentication support, including two-factor authentication (2FA) and SAML 2.0
• Robust onboard hardware security features like a self-encrypted SSD and UEFI Secure Boot
• An open, Linux-based OS that supports Guest OS and Docker containers for third-party software
• Support for zero-touch provisioning (ZTP), custom scripts, and third-party automation tools
• A vendor-neutral, centralized management and orchestration platform for all connected solutions

These characteristics give organizations greater resilience, enabling them to continue operating and providing services in a degraded fashion while recovering from outages and ransomware. In addition, vendor-neutral support for legacy devices and third-party automation enables companies to scale their operations efficiently without costly upgrades.

Why choose Nodegrid terminal servers?

Only one terminal server provides all the features listed above on a completely vendor-neutral platform – the Nodegrid solution from ZPE Systems.

The Nodegrid S Series terminal server uses auto-sensing ports to discover legacy and mixed-vendor infrastructure solutions and bring them under one unified management umbrella.

The Nodegrid Serial Console Plus (NSCP) is the first terminal server to offer 96 management ports on a 1U rack-mounted device (Patent No. 9,905,980).

ZPE also offers integrated branch/edge services routers with terminal server functionality, so you can consolidate your infrastructure while extending your capabilities.

All Nodegrid devices offer a variety of OOB and failover options to ensure maximum speed and reliability. They’re protected by comprehensive onboard security features like TPM 2.0, self-encrypted disk (SED), BIOS protection, Signed OS, and geofencing to keep malicious actors off the management network. They also run the open, Linux-based Nodegrid OS, supporting Guest OS and Docker containers so you can host third-party applications for automation, security, AIOps, and more. Nodegrid extends automation, security, and control to all the legacy and mixed-vendor devices on your network and unifies them with a centralized, vendor-neutral management platform for ultimate scalability, resilience, and efficiency.

Want to learn more about Nodegrid terminal servers?

ZPE Systems offers terminal server solutions for data center, branch, and edge deployments. Schedule a free demo to see Nodegrid terminal servers in action.

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As today’s enterprises race toward digital transformation with cloud-based applications, software-as-a-service (SaaS), and artificial intelligence (AI), data center architectures are evolving. Organizations rely less on traditional server-based infrastructures, preferring the scalability, speed, and cost-efficiency of cloud and hybrid-cloud architectures using major platforms such as AWS and Google. These digital services are supported by an underlying infrastructure comprising thousands of servers, GPUs, and networking devices in what’s known as a hyperscale data center.

The size and complexity of hyperscale data centers present unique management, scaling, and resilience challenges that providers must overcome to ensure an optimal customer experience. This blog explains what a hyperscale data center is and compares it to a normal data center deployment before discussing the unique challenges involved in managing and supporting a hyperscale deployment.

What is a hyperscale data center?

As the name suggests, a hyperscale data center operates at a much larger scale than traditional enterprise data centers. A typical data center houses infrastructure for dozens of customers, each containing tens of servers and devices. A hyperscale data center deployment supports at least 5,000 servers dedicated to a single platform, such as AWS. These thousands of individual machines and services must seamlessly interoperate and rapidly scale on demand to provide a unified and streamlined user experience.

The biggest hyperscale data center challenges

Operating data center deployments on such a massive scale is challenging for several key reasons.