The healthcare industry enthusiastically adopted Internet of Things (IoT) technology to improve diagnostics, health monitoring, and overall patient outcomes. The data generated by healthcare IoT devices is processed and used by sophisticated data analytics and artificial intelligence applications, which traditionally live in the cloud or a centralized data center. Transmitting all this sensitive data back and forth is inefficient and increases the risk of interception or compliance violations.
Edge computing deploys data analytics applications and computing resources around the edges of the network, where much of the most valuable data is created. This significantly reduces latency and mitigates many security and compliance risks. In a healthcare setting, edge computing enables real-time medical insights and interventions while keeping HIPAA-regulated data within the local security perimeter. This blog describes six potential edge computing use cases in healthcare that take advantage of the speed and security of an edge computing architecture.
6 Edge computing use cases in healthcare
Edge computing use cases for EMS
Mobile emergency medical services (EMS) teams need to make split-second decisions regarding patient health without the benefit of a doctorate and, often, with spotty Internet connections preventing access to online drug interaction guides and other tools. Installing edge computing resources on cellular edge routers gives EMS units real-time health analysis capabilities as well as a reliable connection for research and communications. Potential use cases include: .
Use cases
Description
1. Real-time health analysis en route
Edge computing applications can analyze data from health monitors in real-time and access available medical records to help medics prevent allergic reactions and harmful medication interactions while administering treatment.
2. Prepping the ER with patient health insights
Some edge computing devices use 5G/4G cellular to livestream patient data to the receiving hospital, so ER staff can make the necessary arrangements and begin the proper treatment as soon as the patient arrives.
Edge computing use cases in hospitals & clinics
Hospitals and clinics use IoT devices to monitor vitals, dispense medications, perform diagnostic tests, and much more. Sending all this data to the cloud or data center takes time, delaying test results or preventing early intervention in a health crisis, especially in rural locations with slow or spotty Internet access. Deploying applications and computing resources on the same local network enables faster analysis and real-time alerts. Potential use cases include: .
Use cases
Description
3. AI-powered diagnostic analysis
Edge computing allows healthcare teams to use AI-powered tools to analyze imaging scans and other test results without latency or delays, even in remote clinics with limited Internet infrastructure.
4. Real-time patient monitoring alerts
Edge computing applications can analyze data from in-room monitoring devices like pulse oximeters and body thermometers in real-time, spotting early warning signs of medical stress and alerting staff before serious complications arise.
Edge computing use cases for wearable medical devices
Wearable medical devices give patients and their caregivers greater control over health outcomes. With edge computing, health data analysis software can run directly on the wearable device, providing real-time results even without an Internet connection. Potential use cases include: .
Use cases
Description
5. Continuous health monitoring
An edge-native application running on a system-on-chip (SoC) in a wearable insulin pump can analyze levels in real-time and provide recommendations on how to correct imbalances before they become dangerous.
6. Real-time emergency alerts
Edge computing software running on an implanted heart-rate monitor can give a patient real-time alerts when activity falls outside of an established baseline, and, in case of emergency, use cellular and ATT FirstNet connections to notify medical staff.
The benefits of edge computing for healthcare
Using edge computing in a healthcare setting as described in the use cases above can help organizations:
Improve patient care in remote settings, where a lack of infrastructure limits the ability to use cloud-based technology solutions.
Process and analyze patient health data faster and more reliably, leading to earlier interventions.
Increase efficiency by assisting understaffed medical teams with diagnostics, patient monitoring, and communications.
Mitigate security and compliance risks by keeping health data within the local security perimeter.
Edge computing can also help healthcare organizations lower their operational costs at the edge by reducing bandwidth utilization and cloud data storage expenses. Another way to reduce costs is by using consolidated, vendor-neutral solutions to host, connect, and secure edge applications and workloads.
For example, the Nodegrid Gate SR is an integrated branch services router that delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. Nodegrid’s open, Linux-based OS supports VMs and Docker containers for third-party edge applications, security solutions, and more. Plus, an onboard Nvidia Jetson Nano card is optimized for AI workloads at the edge, significantly reducing the hardware overhead costs of using artificial intelligence at remote healthcare sites. Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in healthcare, future-proofing your edge architecture.
Streamline your edge deployment with Nodegrid
The vendor-neutral Nodegrid platform consolidates an entire edge technology stack into a unified, streamlined solution. Watch a demo to see Nodegrid’s healthcare network solutions in action.
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. .
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.
Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and feature roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology (OT) deployments.
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.
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.
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.
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.
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 AccessTM (ZIA)– SWG cyberthreat protection and zero-trust access to SaaS apps and the web.
Zscaler Private AccessTM (ZPA)– ZTNA connectivity to private apps and OT devices.
Zscaler Digital ExperienceTM (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.
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.
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.
Organizations across industries are expanding their digital capabilities and global reach by deploying Internet of Things (IoT) devices, automated operational technology (OT) sites, branch offices, and other tech at the network’s edges. Edge technology transmits vast quantities of data to and from data warehouses, machine learning training systems, and software applications. Traditionally, organizations host some or all of these services in centralized data centers, which is known as on-premises computing.
This approach creates challenges that impact the efficiency and safety of edge operations. As edge data volumes grow, so do MPLS bandwidth costs. Large data transmissions to and from the edge are also at risk of interception by malicious actors. The best way to solve this problem is with edge computing, which moves data processing applications and systems to the edges of the network to run alongside the devices that generate most of the edge data.
This guide defines edge computing vs on-premises computing in detail before analyzing the advantages and challenges involved with each approach.
Defining edge computing vs on-premises computing
On-premises computing systems are physical or virtual resources that live in a traditional data center. Despite the name, these systems don’t necessarily reside in the same physical premises as the main business, with many companies using colocation data centers owned by third parties. Organizations have complete control over the physical and virtual infrastructure, unlike in private or public cloud deployments. The defining characteristic of on-premises computing is that most or all enterprise applications and digital services reside in a centralized location, with most network traffic and data transmissions flowing through it.
Edge computing systems are physical and virtual data processing resources that companies deploy alongside the edge devices that generate the most data. Examples include installing machine learning software at a remote manufacturing site to gain maintenance insights into remote SCADA (supervisory control and data acquisition) systems, or running a data analytics app on a chip installed in a wearable medical sensor to provide patients with real-time health feedback. Edge computing has many potential use cases and deployment models, but the defining characteristic is proximity to the sources of edge-generated data.
Edge Computing vs. On-Premises Computing
Edge Computing
On-Premises Computing
Deployed at the edges of the network
Processes data on-site
Decentralizes enterprise network traffic
Deployed in centralized data centers
Processes data off-site
Requires network traffic and data to flow through a single location
The advantages of edge computing vs on-premises
The benefits of edge computing compared to on-premises include:
Improved workload efficiency – Edge computing reduces network traffic bottlenecks and latency because data stays on the local network or even on the same device. This improves the overall speed, performance, and efficiency of all enterprise applications and services.
Bandwidth cost reduction – Edge computing reduces the volume of data transmitted over MPLS links between edge sites and the central data center. The cost for MPLS bandwidth is typically very high, so edge computing decreases operational costs at branch offices and other edge business sites.
Better data security – Any time companies transmit data off-site, there’s a risk of interception by cybercriminals. Edge computing reduces the attack surface by keeping valuable data on the local network, which improves data security and simplifies data privacy compliance.
The challenges of edge computing vs on-premises
The challenges of edge computing compared to on-premises include:
Data storage restraints – The typical edge deployment is much smaller than a centralized data center and has fewer data storage resources, making it difficult to hold on to data long enough to process it with edge applications.
Fewer security controls – Edge deployments often lack the robust physical security controls utilized by data centers, such as security guards and biometric door locks, creating the need for edge-specific security solutions to protect data and devices.
Edge management and orchestration – Edge sites are difficult for centralized IT operations teams to monitor and troubleshoot, especially if an equipment failure, ransomware attack, or natural disaster takes down the network.
Comparing edge computing vs on-premises
The Pros and Cons of Edge Computing vs On-Premises Computing
Pros of Edge Computing
Cons of Edge Computing
Reduces network bottlenecks and latency for greater workload efficiency across the enterprise
Decreases MPLS bandwidth usage to make edge sites more cost-effective
Keeps edge data on the local network to prevent interception
Edge deployments have less data storage capacity
Edge sites lack the physical security provided by a data center
Network outages prevent remote teams from accessing edge infrastructure.
Edge computing solves many of the challenges involved in processing data at the edges of the network, but it also creates new problems. The best way to ensure edge computing success is to start with a comprehensive strategy that identifies potential hurdles and the technology and operational practices needed to overcome them. For example, zero trust security policies, proactive patch management, and isolated management infrastructure (IMI) help organizations defend edge deployments without the benefit of secure data center facilities. Environmental monitoring, out-of-band (OOB) management, and edge management and orchestration (EMO) platforms all give teams greater control over remote edge infrastructure.
ZPE Systems provides edge network solutions to help you overcome your biggest challenges. Nodegrid integrated edge routers support VM and Docker hosting for your choice of third-party edge computing and security applications, allowing you to devote more hardware budget (and rack space) to data storage and other critical infrastructure. Robust onboard security features like TPM and geofencing defend Nodegrid hardware from tampering and compromise for better edge security coverage.
All Nodegrid devices provide OOB management to give teams continuous remote access to edge infrastructure, allowing them to quickly recover from outages, equipment failures, and cyberattacks. Plus, our vendor-neutral management software seamlessly integrates all your edge solutions to create a unified EMO platform that streamlines edge operations.
Want to learn more about howNodegrid simplifies your network edge?
Request a free demo to learn how Nodegrid can help you overcome the challenges of edge computing vs on-premises computing.
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.
Hyperscale Data Center Challenges
Complexity
Hyperscale data center infrastructure is extensive and complex, with thousands of individual devices, applications, and services to manage. This infrastructure is distributed across multiple facilities in different geographic locations for redundancy, load balancing, and performance reasons. Efficiently managing these resources is impossible without a unified platform, but different vendor solutions and legacy systems may not interoperate, creating a fragmented control plane.
Scaling
Cloud and SaaS customers expect instant, streamlined scaling of their services, and demand can fluctuate wildly depending on the time of year, economic conditions, and other external factors. Many hyperscale providers use serverless, immutable infrastructure that’s elastic and easy to scale, but these systems still rely on a hardware backbone with physical limitations. Adding more compute resources also requires additional management and networking hardware, which increases the cost of scaling hyperscale infrastructure.
Resilience
Customers rely on hyperscale service providers for their critical business operations, so they expect reliability and continuous uptime. Failing to maintain service level agreements (SLAs) with uptime requirements can negatively impact a provider’s reputation. When equipment failures and network outages occur - as they always do, eventually - hyperscale data center recovery is difficult and expensive.
Overcoming hyperscale data center challenges requires unified, scalable, and resilient infrastructure management solutions, like the Nodegrid platform from ZPE Systems.
How Nodegrid simplifies hyperscale data center management
The Nodegrid family of vendor-neutral serial console servers and network edge routers streamlines hyperscale data center deployments. Nodegrid helps hyperscale providers overcome their biggest challenges with:
A unified, integrated management platform that centralizes control over multi-vendor, distributed hyperscale infrastructures.
The open, Linux-based Nodegrid OS which hosts or integrates your choice of third-party software to consolidate functions in a single box.
Fast, reliable out-of-band (OOB) management and 5G/4G cellular failover to facilitate easy remote recovery for improved resilience.
The Nodegrid platform gives hyperscale providers single-pane-of-glass control over multi-vendor, legacy, and distributed data center infrastructure for greater efficiency. With a device like the Nodegrid Serial Console Plus (NSCP), you can manage up to 96 devices with a single piece of 1RU rack-mounted hardware, significantly reducing scaling costs. Plus, the vendor-neutral Nodegrid OS can directly host other vendors’ software for monitoring, security, automation, and more, reducing the number of hardware solutions deployed in the data center.
Nodegrid’s out-of-band (OOB) management creates an isolated control plane that doesn’t rely on production network resources, giving teams a lifeline to recover remote infrastructure during outages, equipment failures, and ransomware attacks. The addition of 5G/4G LTE cellular failover allows hyperscale providers to keep vital services running during recovery operations so they can maintain customer SLAs.
Want to learn more about Nodegrid hyperscale data center solutions from ZPE Systems?
Nodegrid’s vendor-neutral hardware and software help hyperscale cloud providers streamline their operations with unified management, enhanced scalability, and resilient out-of-band management. Request a free Nodegrid demo to see our hyperscale data center solutions in action.
In a healthcare organization, IT’s goal is to ensure network and system stability to improve both patient outcomes and ROI. The National Institutes of Health (NIH) provides many recommendations for how to achieve these goals, and they place a heavy focus on resilience engineering (RE). Resilience engineering enables a healthcare organization to resist and recover from unexpected events, such as surges in demand, ransomware attacks, and network failures. Resilient architectures allow the organization to continue operating and serving patients during major disruptions and to recover critical systems rapidly.
This guide to healthcare network design describes the core technologies comprising a resilient network architecture before discussing how to take resilience engineering to the next level with automation, edge computing, and isolated recovery environments.
A resilient healthcare network design includes resilience systems that perform critical functions while the primary systems are down. The core technologies and capabilities required for resilience systems include:
Full-stack networking – Routing, switching, Wi-Fi, voice over IP (VoIP), virtualization, and the network overlay used in software-defined networking (SDN) and software-defined wide area networking (SD-WAN)
Full compute capabilities – The virtual machines (VMs), containers, and/or bare metal servers needed to run applications and deliver services
Storage – Enough to recover systems and applications as well as deliver content while primary systems are down
These are the main technologies that allow healthcare IT teams to reduce disruptions and streamline recovery. Once organizations achieve this base level of resilience, they can evolve by adding more automation, edge computing, and isolated recovery infrastructure.
Extending automated control over healthcare networks
Automation is one of the best tools healthcare teams have to reduce human error, improve efficiency, and ensure network resilience. However, automation can be hard to learn, and scripts take a long time to write, so having systems are easily deployable with low technical debt is critical. Tools like ZTP (zero-touch provisioning), and the integration of technology like Infrastructure as Code (IaC), accelerate recovery by automating device provisioning. Healthcare organizations can use automation technologies such as AIOps with resilience systems technologies like out-of-band (OOB) management to monitor, maintain, and troubleshoot critical infrastructure.
Using automation to observe and control healthcare networks helps prevent failures from occuring, but when trouble does actually happen, resilience systems ensure infrastructure and services are quickly returned to health or rerouted when needed.
Improving performance and security with edge computing
The healthcare industry is one of the biggest adopters of IoT (Internet of Things) technology. Remote, networked medical devices like pacemakers, insulin pumps, and heart rate monitors collect a large volume of valuable data that healthcare teams use to improve patient care. Transmitting that data to a software application in a data center or cloud adds latency and increases the chances of interception by malicious actors. Edge computing for healthcare eliminates these problems by relocating applications closer to the source of medical data, at the edges of the healthcare network. Edge computing significantly reduces latency and security risks, creating a more resilient healthcare network design.
Note that teams also need a way to remotely manage and service edge computing technologies. Find out more in our blog Edge Management & Orchestration.
Increasing resilience with isolated recovery environments
Ransomware is one of the biggest threats to network resilience, with attacks occurring so frequently that it’s no longer a question of ‘if’ but ‘when’ a healthcare organization will be hit.
Read about the high-profile ransomware attacks affecting MOVEit customers and MGM
Recovering from ransomware is especially difficult because of how easily malicious code can spread from the production network into backup data and systems. The best way to protect your resilience systems and speed up ransomware recovery is with an isolated recovery environment (IRE) that’s fully separated from the production infrastructure.
An IRE ensures that IT teams have a dedicated environment in which to rebuild and restore critical services during a ransomware attack, as well as during other disruptions or disasters. An IRE does not replace a traditional backup solution, but it does provide a safe environment that’s inaccessible to attackers, allowing response teams to conduct remediation efforts without being detected or interrupted by adversaries. Isolating your recovery architecture improves healthcare network resilience by reducing the time it takes to restore critical systems and preventing reinfection.
To learn more about how to recover from ransomware using an isolated recovery environment, download our whitepaper, 3 Steps to Ransomware Recovery.
Resilient healthcare network design with Nodegrid
A resilient healthcare network design is resistant to failures thanks to resilience systems that perform critical functions while the primary systems are down. Healthcare organizations can further improve resilience by implementing additional automation, edge computing, and isolated recovery environments (IREs).
Nodegrid healthcare network solutions from ZPE Systems simplify healthcare resilience engineering by consolidating the technologies and services needed to deploy and evolve your resilience systems. Nodegrid’s serial console servers and integrated branch/edge routers deliver full-stack networking, combining cellular, Wi-Fi, fiber, and copper into software-driven networking that also includes compute capabilities, storage, vendor-neutral application & automation hosting, and cellular failover required for basic resilience. Nodegrid also uses out-of-band (OOB) management to create an isolated management and recovery environment without the cost and hassle of deploying an entire redundant infrastructure.
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Organizations prioritizing digital transformation by adopting IoT (Internet of Things) technologies generate and process an unprecedented amount of data. Traditionally, the systems used to process that data live in a centralized data center or the cloud. However, IoT devices are often deployed around the edges of the enterprise in remote sites like retail stores, manufacturing plants, and oil rigs. Transferring so much data back and forth creates a lot of latency and uses valuable bandwidth. Edge computing solves this problem by moving processing units closer to the sources that generate the data.
IBM estimates there are over 15 billion edge devices already in use. While edge computing has rapidly become a vital component of digital transformation, many organizations focus on individual use cases and lack a cohesive edge computing strategy. According to a recent Gartner report, the result is what’s known as “edge sprawl”: many individual edge computing solutions deployed all over the enterprise without any centralized control or visibility. Organizations with disjointed edge computing deployments are less efficient and more likely to hit roadblocks that stifle digital transformation.
The report provides guidance on building an edge computing strategy to combat sprawl, and the foundation of that strategy is edge management and orchestration (EMO). Below, this post summarizes the key findings from the Gartner report and discusses some of the biggest edge computing challenges before explaining how to solve them with a centralized EMO platform.
Many organizations already use edge computing technology for specific projects and use cases – they have an individual problem to solve, so they deploy an individual solution. Since the stakeholders in these projects usually aren’t architects, they aren’t building their own edge computing machines or writing software for them. Typically, these customers buy pre-assembled solutions or as-a-service offerings that meet their specific needs.
However, a piecemeal approach to edge computing projects leaves organizations with disjointed technologies and processes, contributing to edge sprawl and shadow IT. Teams can’t efficiently manage or secure all the edge computing projects occurring in the enterprise without centralized control and visibility. Gartner urges I&O (infrastructure & operations) leaders to take a more proactive approach by developing a comprehensive edge computing strategy encompassing all use cases and addressing the most common challenges.
Edge computing challenges
Gartner identifies six major edge computing challenges to focus on when developing an edge computing strategy:
Gartner’s 6 edge computing challenges to overcome
Enabling extensibility so edge computing solutions are adaptable to the changing needs of the business.
Extracting value from edge data with business analytics, AIOps, and machine learning training.
Governing edge data to meet storage constraints without losing valuable data in the process.
Supporting edge-native applications using specialized containers and clustering without increasing the technical debt.
Securing the edge when computing nodes are highly distributed in environments without data center security mechanisms.
Edge management and orchestration that supports business resilience requirements and improves operational efficiency.
Let’s discuss these challenges and their solutions in greater depth.
Enabling extensibility – Many organizations deploy purpose-built edge computing solutions for their specific use case and can’t adapt when workloads change or grow. The goal is to attempt to predict future workloads based on planned initiatives and create an edge computing strategy that leaves room for that growth. However, no one can really predict the future, so the strategy should account for unknowns by utilizing common, vendor-neutral technologies that allow for expansion and integration.
Extracting value from edge data – The generation of so much IoT and sensor data gives organizations the opportunity to extract additional value in the form of business insights, predictive analysis, and machine learning training. Quickly extracting that value is challenging when most data analysis and AI applications still live in the cloud. To effectively harness edge data, organizations should look for ways to deploy artificial intelligence training and data analytics solutions alongside edge computing units.
Governing edge data – Edge computing deployments often have more significant data storage constraints than central data centers, so quickly distinguishing between valuable data and destroyable junk is critical to edge ROIs. With so much data being generated, it’s often challenging to make this determination on the fly, so it’s important to address data governance during the planning process. There are automated data governance solutions that can help, but these must be carefully configured and managed to avoid data loss.
Supporting edge-native applications – Edge applications aren’t just data center apps lifted and shifted to the edge; they’re designed for edge computing from the bottom up. Like cloud-native software, edge apps often use containers, but clustering and cluster management are different beasts outside the cloud data center. The goal is to deploy platforms that support edge-native applications without increasing the technical debt, which means they should use familiar container management technologies (like Docker) and interoperate with existing systems (like OT applications and VMs).
Securing the edge – Edge deployments are highly distributed in locations that may lack many physical security features in a traditional data center, such as guarded entries and biometric locks, which adds risk and increases the attack surface. Organizations must protect edge computing nodes with a multi-layered defense that includes hardware security (such as TPM), frequent patches, zero-trust policies, strong authentication (e.g., RADIUS and 2FA), and network micro-segmentation.
Edge management and orchestration – Moving computing out of the climate-controlled data center creates environmental and power challenges that are difficult to mitigate without an on-site technical staff to monitor and respond. When equipment failure, configuration errors, or breaches take down the network, remote teams struggle to meet resilience requirements to keep business operations running 24/7. The sheer number and distribution area of edge computing units make them challenging to manage efficiently, increasing the likelihood of mistakes, issues, or threat indicators slipping between the cracks. Addressing this challenge requires centralized edge management and orchestration (EMO) with environmental monitoring and out-of-band (OOB) connectivity.
A centralized EMO platform gives administrators a single-pane-of-glass view of all edge deployments and the supporting infrastructure, streamlining management workflows and serving as the control panel for automation, security, data governance, cluster management, and more. The EMO must integrate with the technologies used to automate edge management workflows, such as zero-touch provisioning (ZTP) and configuration management (e.g., Ansible or Chef), to help improve efficiency while reducing the risk of human error. Integrating environmental sensors will help remote technicians monitor heat, humidity, airflow, and other conditions affecting critical edge equipment’s performance and lifespan. Finally, remote teams need OOB access to edge infrastructure and computing nodes, so the EMO should use out-of-band serial console technology that provides a dedicated network path that doesn’t rely on production resources.
Gartner recommends focusing your edge computing strategy on overcoming the most significant risks, challenges, and roadblocks. An edge management and orchestration (EMO) platform is the backbone of a comprehensive edge computing strategy because it serves as the hub for all the processes, workflows, and solutions used to solve those problems.
Edge management and orchestration (EMO) with Nodegrid
Nodegrid is a vendor-neutral edge management and orchestration (EMO) platform from ZPE Systems. Nodegrid uses Gen 3 out-of-band technology that provides 24/7 remote management access to edge deployments while freely interoperating with third-party applications for automation, security, container management, and more. Nodegrid environmental sensors give teams a complete view of temperature, humidity, airflow, and other factors from anywhere in the world and provide robust logging to support data-driven analytics.
The open, Linux-based Nodegrid OS supports direct hosting of containers and edge-native applications, reducing the hardware overhead at each edge deployment. You can also run your ML training, AIOps, data governance, or data analytics applications from the same box to extract more value from your edge data without contributing to sprawl.
In addition to hardware security features like TPM and geofencing, Nodegrid supports strong authentication like 2FA, integrates with leading zero-trust providers like Okta and PING, and can run third-party next-generation firewall (NGFW) software to streamline deployments further.
The Nodegrid platform brings all the components of your edge computing strategy under one management umbrella and rolls it up with additional core networking and infrastructure management features. Nodegrid consolidates edge deployments and streamlines edge management and orchestration, providing a foundation for a Gartner-approved edge computing strategy.
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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 
