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Distributed Edge Computing Use Cases

by | Mar 8, 2024 | Application Hosting, Edge Computing, Network Automation, Remote Network Management, Simplify Branch Infrastructure

An industrial worker selecting an illustration of distributed edge computing concepts surrounding the word edge computing
Across every industry, networks are decentralizing as organizations expand with remote business sites, Internet of Things (IoT) deployments, and mobile technologies. Distributed edge computing involves moving data processing systems and applications out of the centralized cloud or data center and distributing them around the network’s edges, where much of the data is generated. As defined by The Open Glossary of Edge Computing, edge native computing integrates with centralized cloud computing resources, local workloads, remote management, and orchestration while having the ability to operate independently.

Edge computing supports secure, real-time data analysis by reducing off-site data transmission. Edge native computing also enables the transition to digital transformation 2.0 by allowing companies to do something with their edge data in real-time, not just collect it. This post discusses six different use cases that could benefit from distributed edge computing, including healthcare, finance, energy, manufacturing, utilities/public services, and AI & machine learning.

Jump to the executive summary.

Distributed edge computing use cases

Use cases for distributed edge computing include:

Healthcare

  • Mitigate security, privacy, and compliance concerns with local data processing, AI, and Zero Touch Provisioned Virtual Network Functions

  • Improve patient health outcomes with real-time alerts that don’t require Internet access

  • Enable emergency mobile medical intervention while reducing mistakes

Finance

  • Support distributed financial networks while reducing security and regulatory risks by managing scope through isolation and built-in change management.

  • Get fast, localized business insights to improve revenue and customer service

  • Deploy AI-powered surveillance and security solutions without network bottlenecks

Energy

  • Enable real-time data processing and ensure network access for air-gapped and isolated environments with IT and OT operations. without network access

  • Improve efficiency with predictive maintenance recommendations and other insights

  • Proactively identify and remediate safety, quality, and compliance issues

Manufacturing

  • Get real-time, data-driven insights to improve manufacturing efficiency and product quality

  • Reduce the risk of confidential production data falling into the wrong hands during transit

  • Ensure continuous communications and operations during network outages and other adverse events

Utilities/Public Services

  • Use IoT technology to deliver better services, improve public safety, and keep communities connected

  • Reduce the fleet management challenges involved in difficult deployment environments

  • Provide IT with reliable remote access to install critical security patches and maintain devices

  • Aid in Disaster Recovery and resilience

AI & Machine Learning

  • Get enhanced data analytics capabilities for any distributed edge computing use case

  • Improve AI/ML efficiency by eliminating network bottlenecks and reducing security risks

  • Use edge devices with a built-in networking stack to improve the agility, cost-effectiveness, and scalability of edge AI/ML
Migration from On Premises to Edge Computing

Image: Concrete use case that can work across all industries, showing the migration from on-prem computing to microservices at the edge, along with the associated level of security risk.

Healthcare

The healthcare industry quickly and enthusiastically adopted IoT technology for medical equipment like insulin pumps, pacemakers, and imaging devices to improve patient health monitoring and outcomes. These sensors generate massive quantities of data that healthcare organizations must transmit to applications in central data centers or the cloud for processing. This data can’t be transferred over the open Internet for security and compliance reasons, so it’s usually funneled through a central firewall via MPLS (for branches, clinics, and other physical sites), overlay networks, or SD-WAN (for wearable sensors and mobile EMS devices). The firewall becomes a bottleneck that increases latency and prevents real-time data processing, introducing potentially lethal delays in health monitoring and response.

Distributed edge computing for healthcare involves installing medical data processing applications closer to the sensors and devices generating most of the data. Edge computing occurs on the same local network or even the same onboard chip (using system-on-chip or SoC technology), which reduces security risks and latency. For example, software running on an implanted heart-rate monitor can analyze patient data in real time without a network connection. If it detects any concerning activity that falls outside of an established baseline, it uses multiple cellular and ATT FirstNet connections to send alerts to the cardiologist without exposing any private patient data. Even if the application can’t establish a network connection at all, the device itself can alert the patient that there’s a problem so they can take immediate action.

Another healthcare use case is mobile EMS units processing patient health data en route to the hospital using edge compute resources built into cellular edge routers. Edge native applications can help medics prevent allergic reactions and harmful medication interactions when administering treatment.

Finance

Finance industry networks are typically highly decentralized, using branches, web and mobile applications, and self-service ATMs to make their services accessible to customers around the world. Banks and other institutions know that edge data has value beyond the financial transactions being conducted, so they use data analytics software (often powered by AI & machine learning) to gain insights into how to improve their services and generate more revenue. However, there are enormous security, regulatory, and reputational risks involved in transmitting sensitive financial data, making it challenging to leverage cloud- or data center-based analytics software.

Distributed edge computing moves financial data processing applications to branches and 26remote PoPs (points of presence) to help mitigate the risks of transmitting data off-site. For example, financial institutions can install all-in-one branch gateway services routers with built-in edge compute functionality in networking closets, drive-up kiosks, or even inside an ATM’s housing. Running data analytics software from this device enables real-time data processing for business insights, surveillance, customer service improvements, and more. These routers should also include out-of-band (OOB) management technology to support infrastructure isolation and simplify compliance with PCI DSS 4.0 and other regulations.

Energy

Edge data in the oil and gas industry comes from IoT sensors and automated equipment deployed in remote sites, drilling rigs, and offshore platforms all over the world. Analyzing that data is crucial for productivity, safety, and compliance, but it’s often difficult to maintain a fast and reliable network connection with applications in data centers or the cloud.

Distributed edge computing allows oil and gas companies to effectively harness their data in challenging deployment environments, such as the middle of the ocean. For example, companies can tuck compact, cellular-enabled edge computing devices into maintenance closets or other small compartments to deploy software that analyzes equipment monitoring data, well logs, and borehole logs. This software can provide predictive maintenance recommendations, alert technicians to potential quality or safety issues, and deliver productivity forecasts and insights without requiring an Internet connection.

Manufacturing

Companies across nearly every industry are increasingly automating their manufacturing to improve productivity, lower costs, and reduce errors. To further reduce human involvement, they use software to monitor equipment health, track production costs, schedule preventative maintenance, and perform quality assurance (QA) tasks. This software, which typically runs from the cloud or a centralized data center, relies on data generated by automated operational technology (OT) and other manufacturing machinery. As in the above use cases, transmitting OT back and forth creates latency and security issues. There are additional risks associated with manufacturing operations located overseas, where political instability, disasters, and other external forces could interrupt communications.

Distributed edge computing enables real-time, data-driven insights to improve manufacturing efficiency and elevate product quality. Plus, some edge computing solutions, like the Nodegrid integrated branch services router, provide out-of-band (OOB) management access to remote equipment. OOB management creates a dedicated management network that’s completely isolated from the production network, ensuring continuous remote access to operational technology, monitoring systems, and edge native applications during Internet outages and other adverse events.

Utilities / public services

Many forward-thinking cities are deploying Internet of Things (IoT) devices to improve their utilities and public services and better connect their communities. These “smart cities” collect data from Internet-connected thermostats, parking meters, traffic lights, security cameras, and other devices deployed outdoors, in public facilities, and in citizens’ homes. However, local governments often find it challenging to keep up with fleet management, ensuring all these devices are connected, patched, and up-to-date to prevent breaches and failures.

Distributed edge computing reduces the networking and bandwidth requirements for IoT-enabled utilities, public services, and smart cities. Edge native applications can analyze data on the same sensor or device that generates it, reporting back to a centralized cloud or data center as needed to provide alerts, reports, and visualizations. All-in-one edge networking solutions combine connectivity with compute capabilities and are small enough to fit in utility cabinets, under public benches, or on top of street lights. They provide remote IT teams with easy access to monitor devices, deploy updates, and troubleshoot issues over a reliable, cellular OOB connection. An edge native networking solution should also enable automatic, zero-touch operations to streamline digital fleet management at scale.

AI & machine learning

Artificial intelligence (AI) and machine learning (ML) applications ingest data to train, operate, and make decisions. Much of that data originates at the network’s edges – in fact, there are AI & ML applications for every edge use case and industry listed above. Transmitting vast quantities of data to the cloud or a data center introduces network bottlenecks, latency, and security risks that can prevent organizations from getting the full value out of their AI investment.

Because artificial intelligence is very resource-hungry, edge native computing for AI/ML sometimes looks a little different than in other use cases. A typical edge computing deployment for AI & ML involves racks of high-performance machine learning processing units deployed in edge data centers on the same site as (or very nearby) the devices generating data. This approach works well for large machine-learning workloads occurring in a limited number of deployment sites. A more flexible approach involves using smaller graphics processing units (GPUs) or multi-purpose edge devices to handle individual AI/ML workloads in smaller and more distributed edge deployment sites. These “thin” or “nano” deployments are agile and cost-effective, scaling easily as organizations grow in size and geographic distribution.

Executive summary

  • Distributed edge computing for healthcare improves patient health outcomes and data privacy with SoC applications on wearable medical devices and cellular edge routers in mobile EMS units.
  • Distributed edge computing for the finance industry provides insights into how to improve services and revenue while helping to mitigate security and regulatory risks with on-site data processing and infrastructure isolation.
  • Distributed edge computing helps the energy sector effectively harness critical data from sensors and equipment in challenging deployment environments to improve quality, safety, and productivity.
  • Distributed edge computing for manufacturing helps companies process data from automated machinery and operational technology to improve manufacturing efficiency and elevate product quality.
  • Distributed edge computing for utilities/public services reduces the networking and fleet management challenges for IoT-enabled utilities, public services, and smart cities with all-in-one edge networking solutions, OOB, and zero-touch operations.
  • Distributed edge computing for AI & machine learning uses multi-purpose edge devices to handle individual workloads, improving the agility, scalability, and cost-effectiveness of edge AI/ML.

Distributed edge computing with Nodegrid

Nodegrid is a line of all-in-one edge networking solutions from ZPE Systems. Nodegrid’s vendor-neutral, integrated branch services routers combine edge gateway networking functionality with Gen 3 out-of-band management and edge computing capabilities. The Nodegrid platform streamlines distributed edge computing for any use case with consolidated hardware and software that reduce deployment costs and management headaches while improving efficiency.

See Nodegrid’s edge solutions in action

Nodegrid delivers streamlined, cost-effective solutions for distributed edge computing in healthcare, EMS, financial services, local governments, and more. To see how Nodegrid works for your edge computing use case, request a free demo.

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Edge Computing Ecosystem Design

by | Mar 1, 2024 | Application Hosting, Edge Computing, Remote Network Management, Simplify Branch Infrastructure

A person in a suit taps a glowing edge computing ecosystem with many network connections and glowing icons of edge computing concepts
Edge computing allows companies with highly distributed networks to efficiently process data from remote devices like Internet of Things (IoT) sensors and automated industrial systems. Teams deploy computing resources and data handling applications closer to data sources at the network’s edges, eliminating transmission latency and preventing data from leaving the local security perimeter. The current edge computing ecosystem consists mainly of solutions designed around individual use cases that lack interoperability with each other or a centralized management platform. That means most organizations end up with a disjointed edge computing architecture without any organized strategy.

According to Gartner, companies that deploy edge computing non-strategically are less efficient and lack the agility and scalability to meet their digital transformation goals. This post discusses the challenges created by a fragmented edge computing market before providing edge computing ecosystem design best practices to overcome these hurdles.

Edge computing ecosystem challenges

Most edge computing vendors offer products designed around a single use case or workload, such as analyzing machine logs to provide predictive maintenance recommendations for a specialized robotic manufacturing arm. These solutions don’t interoperate with each other or integrate with centralized orchestration platforms from other vendors, so each one is managed independently, often by the individual departments that use them. This fragmented architecture creates three major problems that prevent organizations from operating securely and efficiently: shadow IT, edge sprawl, and a lack of edge resilience.

Edge Computing Ecosystem Challenges

Shadow IT

Shadow IT occurs when individual departments or users purchase technology solutions without the knowledge, approval, or support of IT. Shadow IT is dangerous because these solutions aren’t onboarded with security controls and monitoring tools, so they are vulnerable to cybercriminals. Organizations also might purchase edge computing solutions with overlapping capabilities without realizing it, needlessly increasing operational costs.

Edge Sprawl

Edge sprawl occurs when there are so many different edge computing solutions that an organization can’t effectively manage them all. Teams often struggle to stay on top of patch schedules, leaving vulnerabilities in edge devices critically exposed. They also lack the ability to monitor and optimize performance, reducing the efficiency of edge computing operations. 

Poor Resilience

Edge computing deployments typically lack the climate control, physical security, and technical oversight of centralized data centers, increasing the likelihood of environmental issues and limiting IT’s ability to respond to them. Complex edge deployments are also at high risk of human error, and network outages prevent remote teams from quickly troubleshooting and recovering.
Gartner’s best practices for overcoming these challenges is a vendor-neutral edge management and orchestration (EMO) platform that unifies edge computing solutions and gives teams a complete, 360-degree overview of edge operations. This EMO should use out-of-band (OOB) management technology to ensure 24/7 accessibility during production network outages and breaches. Additionally, the platform should integrate with edge automation solutions like zero-touch provisioning and AIOps to improve efficiency and reduce the risk of human error.
A diagram showing how to use ZPE to follow Gartner’s best practices for an isolated management infrastructure.
Additionally, exposed management interfaces represent a major threat to edge resilience because attackers who breach the network could take complete control over infrastructure and “crown jewels” assets. Gartner’s recommendation is to move management interfaces to an isolated management infrastructure (IMI) that’s completely separate from the production network. Download our blueprint to learn more.

Edge computing ecosystem design with Nodegrid

The Nodegrid solution from ZPE Systems helps organizations overcome their biggest edge computing challenges with a unified, vendor-neutral platform. With compact, all-in-one edge networking solutions like the Bold SR, you can consolidate your edge infrastructure for streamlined, cost-effective deployments. For challenging outdoor or mobile deployments, the Mini SR delivers networking, automation, and OOB in a smartphone-sized device that fits anywhere.

Nodegrid’s vendor-neutral, out-of-band management platform gives teams a lifeline to monitor, troubleshoot, and recover edge infrastructure during cyber attacks and outages, improving edge resilience and reducing business disruption. Plus, our environmental sensors provide crucial data about temperature, humidity, and other conditions so teams can proactively address issues before a failure occurs.

Nodegrid’s management platform, available as an on-premises or cloud-based application, unifies all your edge computing solutions under one roof. Teams can view monitoring dashboards, deploy patches, perform device maintenance, orchestrate automated workflows, and more from one centralized, vendor-neutral portal.

Maximize edge computing efficiency, security, and resilience

Using the Nodegrid edge management and orchestration platform as the foundation for your edge computing ecosystem design helps maximize the efficiency, security, and resilience of edge deployments. Contact ZPE Systems to learn more.

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Zero Trust Edge Solutions: Continuing the Zero Trust Journey

by | Feb 28, 2024 | Application Hosting, Increase Productivity, Monitoring & Reporting, NetDevOps, Network Automation, Out of Band Management, Remote Network Management, Serial Consoles, Simplify Branch Infrastructure, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP)

A glowing shield with a 0 on it overlays a glowing map of the world to represent zero trust at the edge.

The zero trust security methodology follows the principle of “never trust, always verify,” which assumes that any account or device could be compromised and should be forced to continuously establish trustworthiness. This sounds like an extreme approach, but with the frequency of high-profile data breaches and ransomware attacks steadily increasing, security teams must pivot their approach away from prevention and toward damage mitigation and recovery. Zero trust security limits the lateral movement of compromised accounts on the network by establishing micro-perimeters around network resources that continually assess an account’s behavior for suspicious activity.

Organizations also must extend zero trust security policies and controls to remote business sites at their network’s edges, such as branches, Internet of Things (IoT) deployments, and home offices. Zero trust edge solutions are software platforms that provide networking, access, and security capabilities designed specifically for the edge. This guide explains what zero trust edge solutions do and the challenges involved in using them before discussing how to build a unified ZTE platform.

Table of contents

What are zero trust edge solutions?

A zero trust edge solution combines edge-centric security functionality with remote access and networking capabilities. ZTE’s core feature is zero trust network access (ZTNA), which securely connects remote users to enterprise applications and resources, similar to a VPN. ZTNA is more secure than VPNs because it only allows users to authenticate to one resource at a time and prevents them from seeing or accessing anything else until they re-establish their identity and credentials. ZTE’s other features and capabilities vary depending on the vendor and deployment type. ZTE solutions come in three different forms:

  • As a service: Companies can purchase ZTE functionality as a cloud-based, vendor-managed service. Remote users connect to regional points of presence (POPs) to reach the ZTE stack in the cloud before being routed to enterprise resources. This deployment style is easier to deploy for organizations with lots of users in the field but few (if any) physical edge locations to host security or networking solutions.
    .
  • With SD-WAN: Some ZTE providers combine zero-trust features with software-defined wide area networking (SD-WAN) capabilities. SD-WAN creates a virtual network overlay that’s decoupled from the underlying WAN infrastructure, enabling centralized control and automation. Packaging ZTE and SD-WAN together helps organizations consolidate their tech stack at physical edge sites like branches, warehouses, and manufacturing plants while still offering ZTNA to work-from-home and field employees.
    .
  • Build your own: Since there are very few mature ZTE providers on the market, and it can be difficult to find pre-made solutions with all the features needed for complex, distributed edge networks, many teams opt to build their own platform by combining tools from multiple vendors. Typically, these organizations have physical branches with existing WAN infrastructure that they use as regional POPs to host ZTNA and other security solutions.

Why build your own ZTE solution?

If pre-made solutions exist, why would companies go through the hassle of creating their own zero trust edge platform? Presently, there aren’t any “complete” ZTE solutions that offer full, zero-trust protection for branches and other physical edge sites.

For example, many ZTE platforms don’t protect management ports on the control plane, leaving critical edge infrastructure like servers, switches, and power distribution units (PDUs) exposed to cybercriminals. Additionally, branch ZTE solutions rely upon production network infrastructure, so if there’s an outage or ransomware attack, remote management teams are completely cut off from troubleshooting and recovery. These solutions also lack helpful edge networking features like fleet management and automation, and their closed ecosystems limit the ability to extend their capabilities.

Building your own zero trust edge platform allows you to combine all the security, networking, and management functionality you need to get full security coverage and streamline branch operations. The key to creating a robust and efficient ZTE solution is starting with a vendor-neutral platform that can unify the entire security architecture.

How Nodegrid simplifies ZTE

Nodegrid edge networking solutions from ZPE Systems provide the perfect vendor-neutral platform for integrated zero trust edge deployments. All-in-one edge gateway routers deliver a full stack of branch networking capabilities, including out-of-band (OOB) management. OOB creates a dedicated control plane on an isolated network so remote teams have continuous access to manage, troubleshoot, and repair edge infrastructure.

Nodegrid protects the management interfaces on the OOB network with robust, zero trust security processes and controls. For example, the encryption keys for each Nodegrid device are destroyed after provisioning so that only the public key is accessible when needed for authentication to our cloud. Nodegrid devices also use the Trusted Platform Module (TPM) as a hardware security module to prevent cybercriminals from tampering with the configuration or storage.

Our platform runs on the Linux-based, x86 Nodegrid OS, which supports VMs and Docker containers for third-party applications. That means you can deploy ZTNA, SD-WAN, and other zero trust edge solutions without purchasing or managing additional hardware at each branch. Nodegrid’s OOB and failover functionality ensure those security and access solutions remain operational during ISP outages, ransomware attacks, and other disruptions. Teams can also run their favorite tools for automation, troubleshooting, and recovery on the Nodegrid platform, streamlining edge operations and ensuring their toolbox is available on the OOB network. Nodegrid also simplifies fleet management with true zero-touch provisioning to securely and automatically deploy configurations at edge business sites.

Want to unify your zero trust edge solutions with Nodegrid?

Nodegrid provides a robust, vendor-neutral platform to unify and extend your zero trust edge capabilities. Request a free demo to see Nodegrid in action. Watch Demo

IT Automation vs Orchestration: What’s the Difference?

by | Feb 26, 2024 | Application Hosting, Increase Productivity, Monitoring & Reporting, NetDevOps, Network Automation, Out of Band Management, Remote Network Management, Serial Consoles, Simplify Branch Infrastructure, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP)

it-automation-vs-orchestration

IT automation and orchestration are two important concepts in the field of information technology that are often used interchangeably but are actually quite different. IT automation focuses on individual tasks, whereas orchestration encompasses multiple tasks or even entire workflows. Each approach produces different results and helps teams meet different goals. They also have their own benefits and challenges that must be considered. This guide compares IT automation vs orchestration to clear up misconceptions and help organizations choose the right approach to streamlining their IT operations.

Table of contents
    1. IT automation vs orchestration: What’s the difference?
    2. The benefits of IT automation vs orchestration
    3. The challenges of IT automation and orchestration
    4. Tips for overcoming IT automation and orchestration challenges
    5. Choosing IT automation vs orchestration
    6. IT automation vs orchestration simplified

IT Automation vs Orchestration: What’s the Difference?

IT Automation vs Orchestration

IT automation refers to the use of technology to automate repetitive tasks and processes, including things like automated backups, software updates, and monitoring systems. The goal of IT automation is to free up time and resources for IT professionals by automating routine tasks, allowing them to focus on more strategic initiatives.

Orchestration, on the other hand, is the coordination and management of multiple processes or entire workflows. This can include things like configuring and deploying new servers, managing network connections, and monitoring the performance of many different systems. The goal of orchestration is to improve the overall efficiency of IT operations, reducing costs and enabling greater scalability.

The benefits of IT automation vs orchestration

Benefits of IT Automation vs Orchestration

IT Automation

  • Saves time
  • Reduces human error
  • Improves compliance

Orchestration

  • Increases operational efficiency
  • Improves network scalability
  • Ensures IT system reliability

One of the main benefits of IT automation is that it can save time and resources for IT professionals. By automating routine tasks, IT teams can focus on more strategic initiatives and projects. Additionally, automation helps reduce human error and increases the accuracy, speed, and efficiency of tasks. Automation also improves compliance, as automated processes are less prone to human negligence and are easier to audit.

Orchestration, on the other hand, helps improve the overall efficiency and effectiveness of IT operations. By automating the coordination and management of multiple tasks, orchestration helps ensure that different systems and processes work together seamlessly. Additionally, orchestration helps improve the scalability and reliability of IT systems by ensuring different components are configured and deployed correctly.

The challenges of IT automation and orchestration

IT Automation and Orchestration Challenges

IT Complexity

Teams can’t effectively automate IT operations unless they thoroughly understand all the tasks, systems, and workflows comprising a highly complex network.

Automation Skills Gap

A high demand for automation engineers makes it difficult and expensive to recruit, train, and retain qualified IT automation and orchestration professionals.

Supporting Infrastructure

Effective automation and orchestration deployments require a robust underlying infrastructure of specialized hardware and software solutions.

One of the main challenges of automation and orchestration is the complexity of IT systems. As organizations rely more heavily on specialized technology and grow both in size and in number of business sites, IT systems become increasingly complex and difficult to manage. Automation and orchestration help reduce complexity by automating routine tasks and coordinating the management of different systems. However, teams must understand those tasks and systems well enough to know how to automate them effectively; otherwise, mistakes will proliferate or there will be gaps in automated workflows.

Another IT automation and orchestration challenge is the need for skilled professionals to deploy and manage these solutions. As automation and orchestration become more prevalent, the demand for skilled professionals has increased, making it harder (and more expensive) to recruit and retain qualified automation engineers. The alternative is for organizations to spend time and resources training existing IT staff to work with automation and orchestration.

Additionally, organizations need to invest in the technology and infrastructure necessary to support automation and orchestration. Some examples of these automation infrastructure components include:

  • Gen 3 out-of-band (OOB) serial consoles, which allow teams to deploy third-party automation on an OOB network that doesn’t rely on production infrastructure, improving security and resilience. Gen 3 OOB also moves bandwidth-hogging orchestration workflows off the production network, which reduces latency for better performance.
  • Software-defined networking, which virtualizes the control and management processes and abstracts them from underlying LAN and WAN hardware. SDN, SD-WAN, and SD-Branch technologies enable a high degree of automation for networking workflows such as load balancing, application-aware routing, and failover.
  • Infrastructure as Code (IaC), which turns infrastructure configurations into software code. IaC enables the use of version control, zero-touch deployments, automatic configuration management, automated security testing, and other tools and processes that support automation and improve network resilience.
  • Orchestrator software, which controls all of the automated workflows on a network. The orchestrator is the central hub for teams to create, deploy, monitor, and troubleshoot automated workflows and infrastructure.
  • AIOps, or artificial intelligence for IT operations, which analyzes all the logs and data pulled from automated infrastructure devices and security appliances. AIOps provides predictive maintenance insights, automatic root-cause analysis (RCA), enhanced threat detection, and other functionality to help support a complex, automated network infrastructure.

Tips for overcoming IT automation and orchestration challenges

While every organization will face unique IT automation and orchestration hurdles, there are two basic tips to help simplify any deployment. Using consolidated network hardware and vendor-neutral platforms can help reduce the complexity of network infrastructure, the need to hire additional staff, and the cost to deploy automation infrastructure.

  • Consolidated network hardware, such as all-in-one branch/edge gateway routers, significantly reduces the number of devices deployed at each business site. Fewer devices to automate means less complexity, and organizations save money on deployment costs like hardware overhead and automation license seats.
  • Vendor-neutral platforms, such as the Nodegrid infrastructure management platform from ZPE Systems, allow teams to use the automation and orchestration tools they’re most comfortable with regardless of provider, reducing the skills gap. Open platforms ensure seamless interoperability between all the various automated components to decrease management complexity. Vendor-neutral hardware also allows organizations to run software from multiple vendors on a single device, enabling even greater network consolidation to reduce the complexity and cost of automated infrastructure deployments.

Choosing IT automation vs orchestration

IT automation and orchestration are interconnected concepts that are frequently, but incorrectly, used interchangeably. Automation focuses on individual tasks, while orchestration manages multiple tasks and entire workflows. Both automation and orchestration can help improve the efficiency and effectiveness of IT operations, but they have their unique benefits and challenges. Organizations must carefully consider their IT systems and needs when deciding which approach to use.

IT automation vs orchestration simplified

The network automation experts at ZPE Systems have helped Big Tech brands like Amazon and Uber improve operational efficiency and resilience with IT automation and orchestration. Learn how to use these best practices to streamline your IT operations by downloading our Network Automation Blueprint.

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Edge Computing vs On-Premises: A Comparison

by | Feb 23, 2024 | Application Hosting, Edge Computing, EdgeOps, Remote Network Management

Edge Computing is at the center of a network of hexagons containing icons of edge computing concepts.
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 how Nodegrid 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.

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Network Resilience: What is a Resilience System?

by | Feb 2, 2024 | Application Hosting, Data Center Resilience, Improve Network Security, Increase Productivity, Micro-segmentation, Minimize Impact of Disruptions, Monitoring & Reporting, Network Automation, Out of Band Management, Power Management, Remote Network Management, Serial Consoles, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

A digital web of interconnected network resilience concepts being selected by a business person in a suit.

Network resilience means being able to withstand or recover from adversity, service degradation, and complete outages with minimal business disruption. The longer business-critical services are down, or systems are breached, the greater the risk of significant financial, reputational, and legal consequences. A resilience system is a set of technologies that enable an organization to continue operating while teams work to repair failures and recover from cyberattacks. But what exactly is a resilience system, and what does it look like? This guide to network resilience defines resilience systems, provides example use cases, compares them to related technologies like backups and redundant systems, and describes the key components required to build them.

Table of contents
    1. What is a resilience system?
    2. Resilience system use cases
    3. Do backups and redundancy equate to network resilience?
    4. What does a resilience system look like?
    5. Additional network resilience resources

What is a resilience system?

A resilience system provides all the infrastructure, tools, and services necessary to continue operating, if in a degraded state, during major incidents. It also includes everything needed to recover data, rebuild systems, perform security testing, and continue delivering core business functionality. A resilience system is typically isolated from the production network, preventing cybercriminals from finding and compromising it and ensuring teams have continuous access even if the primary network goes down.

Resilience system use cases

Some examples of the challenges that resilience systems help overcome include:

1. Ransomware recovery

In a ransomware attack, cybercriminals infect systems with malware that spreads throughout the network and encrypts any data it encounters. Modern ransomware now uses packaged attacks that move at machine speed, instantly incapacitating entire networks. Organizations completely lose access to critical systems and data until they pay a ransom, often in untraceable cryptocurrency. Ransomware is an exceptionally tenacious form of malware and tends to reinfect backup data and rebuilt systems, significantly hampering recovery efforts and increasing the duration and cost of the attack. The best practice for resilience systems is to isolate them on an out-of-band (OOB) network, inaccessible to hackers who have breached the production in-band network. Doing so creates a safe, isolated recovery environment (IRE) where teams can restore critical data and systems without the risk of reinfection. The resilience system includes all the tools and hardware needed to restore critical business services and infrastructure. An IRE significantly accelerates ransomware recovery and minimizes downtime, so businesses can avoid paying ransoms and reduce the overall cost of attacks.

2. Network outages

Enterprise network architectures and supply chains are highly complex, with lots of moving parts that rely on external vendors to maintain availability. Just one of those vendors dropping the ball could take the entire organization offline, severely impacting network resilience. For example, in 2023, an expired cryptographic certificate caused Cisco’s Viptela SD-WAN appliances to fail on reboot, completely taking down affected networks until the issue was resolved. With a resilience system, Viptela customers could have potentially avoided this downtime by failing over to alternative network resources. For example, a resilience system with integrated cellular failover allows branches to continue connecting to and delivering critical business services while also providing a lifeline for remote teams to access and recover failed systems. A resilience system also provides observability and automatic notifications so teams are instantly alerted to issues like certificate expirations and can respond quickly to recover critical services.

3. Shift to remote work

Incidents like ransomware attacks and equipment failures happen frequently enough that companies can create detailed plans and proactively implement solutions to minimize their impact, but not all adverse events are so predictable. When the COVID-19 pandemic struck, the massive shift to remote work strained the network resources of most organizations. Instead of maintaining a limited number of branch offices, teams suddenly had to treat every employee as a new branch, leading to performance degradation and outages as they scrambled to reinforce the business’s remote capabilities. A resilience system gives teams the tools and resources they need to provision additional infrastructure, manage networking logic, deploy new security solutions, and more, even while the primary network is offline or under a heavy load. A resilience system is the key to quickly adjusting network performance and security to adapt to sudden changes like a transition to fully remote operations.

Do backups and redundancy equate to network resilience?

The short answer is no; backups and redundancy do not equate to network resilience, though they do contribute to making systems more resilient.

  • Backups are copies of data, configurations, and application code used to do a hot or cold restore when a production system fails. The underlying infrastructure must remain operational for teams to access and use backups, and unless additional resilience measures are taken, it’s easy for backups to become infected or compromised, severely hampering recovery efforts.
  • Redundancy involves duplicating critical systems, services, and applications as a failsafe in case the primaries go down. Organizations can “fail over” to the redundancies to continue critical business operations during outages. However, redundant systems are just as susceptible to failures and infections without additional resilience measures like out-of-band management and isolated management infrastructure.

Backups and redundancy are part of network resilience but alone are not enough to ensure business continuity. Resilience systems focus on maintaining the architecture of the production network while adding the ability to recover or adapt to adversity. The next section discusses all the tools and technologies that make up network resilience systems.

What does a resilience system look like?

There are four key components that go into a resilience system.

Key Components of a Resilience System

Alternative Networking

Full-stack routing and switching, Wi-Fi, VoIP, virtualization, software-defined network overlays for SDN & SD-WAN

Alternative Compute

Full-stack compute, containers, virtual machines, and any other resources needed to run applications and deliver services

Storage & Storage Recovery

Enough storage to recover systems and applications as well as support content delivery

Automation

Tools like zero-touch provisioning (ZTP) to facilitate speedy recovery while minimizing human error

Alternative networking and compute resources ensure the organization can failover in the event of a network failure or continue delivering services when production servers are unavailable. Teams also need enough storage to restore backup data, build new systems, and support the content delivery network (CDN). Automation solutions like zero-touch provisioning (ZTP), configuration management, and security validation tools accelerate the recovery process while mitigating the risk of human error. Combined, these components enable teams to reduce the frequency, severity, and duration of outages, improving overall network resilience.

Network resilience with ZPE Systems

A resilient network will continue delivering critical business services in the face of any challenge, whether from cybercriminals, supply chain issues, global events, or even plain human error. A resilience system is isolated from the production network to ensure security and availability, and it consists of all the tools and technologies needed to troubleshoot, recover, and deliver your most crucial data, applications, and infrastructure. The Nodegrid platform from ZPE Systems is the perfect foundation for a resilience system. Nodegrid is a vendor-neutral, out-of-band management solution capable of running your choice of third-party software. Nodegrid allows you to build a highly customizable IRE containing all the tools needed to safely recover from ransomware. You can even use Nodegrid to deliver services while the primary network or systems are down, making it your all-in-one network resilience multi-tool.

Want to ensure network resilience by accelerating ransomware recovery?

Minimize the business impact of ransomware with the help of our whitepaper, 3 Steps to Ransomware Recovery. Learn how to follow Gartner’s best practices to build an Isolated Recovery Environment

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