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The Future of Edge Computing

by | Mar 18, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, Micro-segmentation, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Virtualization, Zero Trust Security

The Future of Edge Computing
Edge computing moves computing resources and data processing applications out of the centralized data center or cloud, deploying them at the edges of the network and allowing companies to use their edge data in real-time. An explosion in edge data generated by Internet of Things (IoT) sensors, automated operational technology (OT), and other remote devices has created a high demand for edge computing solutions. A recent report from Grand View Research valued the edge computing market size at $16.45 billion in 2023 and predicted it to grow at a compound annual growth rate (CAGR) of 37.9% by 2030.

The current edge computing landscape comprises solutions focused on individual use cases,  lacking interoperability and central orchestration. The future of edge computing, as described by leading analysts at Gartner, depends on unifying the edge computing ecosystem with comprehensive strategies and centralized, vendor-neutral management and orchestration. This future relies on edge-native applications that integrate seamlessly with upstream resources, remote management, and orchestration while still being able to operate independently.

Where is edge computing now?

Many organizations already use edge computing technology to solve individual problems or handle specific workloads. For example, a manufacturing department may deploy an edge computing application to analyze log data and provide predictive maintenance recommendations for a single type of machine or assembly line. A single company may have a dozen or more disjointed edge computing solutions in use throughout the network, creating visibility and management headaches for IT teams. This piecemeal approach to edge computing results in what Gartner calls “edge sprawl”: many disparate solutions deployed without centralized control, security, or visibility. Edge sprawl increases management complexity and risk while decreasing operational efficiency, creating significant roadblocks for digital transformation initiatives.

Additionally, many organizations misunderstand edge computing by thinking it’s just about moving computing resources as close to the edge as possible to collect data. In reality, the true potential of the edge involves using edge data in real-time, gaining “cloud-in-a-box” capability that works in concert with the network’s upstream resources.

Anticipating the future of edge computing

At Gartner’s 2023 IT Infrastructure Operations & Cloud Strategies Conference, edge technology experts predicted that, by 2025, enterprises will create and process more than 50% of their data outside the centralized data center or cloud. Surging edge data volume will accelerate the challenges caused by a lack of strategy or orchestration.

Gartner’s 6 Edge Computing Challenges

Lack of extensibility

Many purpose-built edge computing solutions can’t adapt as use cases change or expand as the business scales, limiting agility and preventing efficient growth.

Inability to extract value from edge data

Much of the valuable data generated by edge sensors and devices gets left on the table, so to speak, because companies lack the resources needed to run all their data analytics and AI apps at the edge and are stuck simply collecting data rather than being able to do much with it.

Data storage constraints

Edge computing deployments are often smaller and have more data storage constraints than large data centers and cloud deployments, but quickly distinguishing between valuable data and destroyable junk is difficult with edge resources.

Knowledge debt from edge-native apps

Edge-native applications are designed for edge computing architectures from the ground up. Edge containers are similar to cloud-native apps, but clustering and cluster management work much differently, creating what’s known as “knowledge debt” and straining IT teams.

Lack of security controls, policies, & visibility

Edge deployments often lack many of the security features used in data centers, and sometimes other departments install edge computing solutions without onboarding them with IT for the application of security policies and monitoring agents, adding risk and increasing the attack surface.

Inability to remotely orchestrate, monitor, & troubleshoot

When equipment failures, configuration errors, or breaches take down edge networks, remote teams are often cut-off and unable to troubleshoot or recover without traveling on-site or paying for managed services, increasing the duration and cost of the outage. Current edge solutions are novel and don’t connect to or integrate with the full networking stack.
At the Gartner conference, analyst Thomas Bittman gave multiple presentations echoing his advice from the Building an Edge Computing Strategy report published earlier in the year. In preparing for the future of edge computing, Bittman urges companies to proactively develop a comprehensive edge computing strategy encompassing all potential use cases and addressing the challenges described above. His recommendations include:

  • Enabling extensibility by utilizing vendor-neutral platforms that allow for expansion and integration, which supports growth and agility at the edge.
  • Looking for opportunities to deploy artificial intelligence, data analytics, and machine learning alongside edge computing units, for example, with system-on-chip technology or all-in-one edge networking and computing devices.
  • Anticipating data storage and governance challenges at the edge by defining clear policies and deploying AI/ML data management solutions that dynamically determine data value.
  • Reducing knowledge debt by utilizing vendor-neutral platforms that support familiar container and cluster management technologies (like Docker and Kubernetes).
  • Securing the edge with a multi-layered defense, including hardware security, frequent patches, zero-trust policies, strong authentication, network micro-segmentation, and comprehensive security monitoring.
  • Centralizing edge management and orchestration (EMO) with a vendor-neutral platform that unifies control, supports environmental monitoring, and uses out-of-band (OOB) management while interoperating with automated edge management workflows (such as zero-touch provisioning and infrastructure configuration management).

Bittman’s recommended edge computing strategy uses the central EMO as a hub for all the technologies, processes, and workflows involved in operating and supporting the edge. This strategy will prepare companies for the future of edge computing and support efficient, agile growth and innovation.

Enter the future of edge computing with Nodegrid

Nodegrid is a vendor-neutral edge management and orchestration platform from ZPE Systems. Nodegrid easily interoperates with your choice of edge solutions and can directly run third-party AI, ML, data analytics, and data governance applications to help you extract more value from your edge data. The open, Linux-based Nodegrid OS can also host Docker containers and edge-native applications to reduce hardware overhead and knowledge debt.

Nodegrid devices protect your edge management interfaces with hardware security features like TPM and geofencing, support for strong authentication like 2FA, and integrations with leading zero-trust providers like Okta and PING. The Nodegrid OS and ZPE Cloud are Synopsys-validated to address security at every stage of the SDLC. Plus, you can run third-party security solutions for SASE, next-generation firewalls, and more.

Nodegrid edge networking solutions use out-of-band technology to give teams 24/7 remote visibility, management, and troubleshooting access to edge deployments. It freely interoperates with third-party solutions for infrastructure automation, monitoring, and recovery to support network resilience and operational efficiency. Nodegrid is like a cloud-in-a-box solution, incorporating edge computing and the full networking stack. Nodegrid’s edge management and orchestration platform provides single-pane-of-glass visibility, control, and resilience while supporting future edge growth.

Use Nodegrid for your Gartner-approved edge computing strategy

The Nodegrid EMO platform helps you anticipate the future of edge computing with vendor-neutral, single-pane-of-glass visibility and control. Watch a free Nodegrid demo to learn more.

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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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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 Doesn’t Mean What it Did 20 Years Ago

by | Jan 25, 2024 | Application Hosting, Data Center Management, DevOps, Edge Computing, Improve Network Security, Micro-segmentation, Minimize Impact of Disruptions, NetDevOps, Network Automation, Remote Network Management, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

Network resilience requirements have changed

Enterprise networks are like air. When they’re running smoothly, it’s easy to take them for granted, as business users and customers are able to go about their normal activities. But when customer service reps are suddenly cut off from their ticketing system, or family movie night turns into a game of “Is it my router, or the network?”, everyone notices. This is why network resilience is critical.

But, what exactly does resilience mean today? Let’s find out by looking at some recent real-world examples, the history of network architectures, and why network resilience doesn’t mean what it did 20 years ago.

Why does network resilience matter?

There’s no shortage of real-world examples showing why network resilience matters. The takeaway is that network resilience is directly tied to business, which means that it impacts revenue, costs, and risks. Here is a brief list of resilience-related incidents that occurred in 2023 alone:

  • FAA (Federal Aviation Administration) – An overworked contractor unintentionally deleted files, which delayed flights nationwide for an entire day.
  • Southwest Airlines – A firewall configuration change caused 16,000 flight cancellations and cost the company about $1 billion.
  • MOVEit FTP exploit – Thousands of global organizations fell victim to a MOVEit vulnerability, which allowed attackers to steal personal data for millions.
  • MGM Resorts – A human exploit and lack of recovery systems let an attack persist for weeks, causing millions in losses per day.
  • Ragnar Locker attacks – Several large organizations were locked out of IT systems for days, which slowed or halted customer operations worldwide.

What does network resilience mean?

Based on the examples above, it might seem that network resilience could mean different things. It might mean having backups of golden configs that you could easily restore in case of a mistake. It might mean beefing up your security and/or replacing outdated systems. It might mean having recovery processes in place.

So, which is it?

The answer is, it’s all of these and more.

Donald Firesmith (Carnegie Mellon) defines resilience this way: “A system is resilient if it continues to carry out its mission in the face of adversity (i.e., if it provides required capabilities despite excessive stresses that can cause disruptions).”

Network resilience means having a network that continues to serve its essential functions despite adversity. Adversity can stem from human error, system outages, cyberattacks, and even natural disasters that threaten to degrade or completely halt normal network operations. Achieving network resilience requires the ability to quickly address issues ranging from device failures and misconfigurations, to full-blown ISP outages and ransomware attacks.

The problem is, this is now much more difficult than it used to be.

How did network resilience become so complicated?

Twenty years ago, IT teams managed a centralized architecture. The data center was able to serve end-users and customers with the minimal services they needed. Being “constantly connected” wasn’t a concern for most people. For the business, achieving resilience was as simple as going on-site or remoting-in via serial console to fix issues at the data center.

Network architecture showing simplicity of data center connected via MPLS to branch office

Then in the mid-2000s, the advent of the cloud changed everything. Infrastructure, data, and computing became decentralized into a distributed mix of on-prem and cloud solutions. Users could connect from anywhere, and on-demand services allowed people to be plugged in around-the-clock. Services for work, school, and entertainment could be delivered anytime, no matter where users were.

Network architecture showing complexity of data center, CDN, remote user, branch office, all connected via many paths

Behind the scenes, this explosion of architecture created three problems for achieving network resilience, which a simple serial could no longer fix:

Too Much Work

Infrastructure, data, and computing are widely distributed. Systems inevitably break and require work, but teams don’t have the staff to keep up.

Too Much Complexity

Pairing cloud and box-based stacks creates complex networks. Teams leave systems outdated, because they don’t want to break this delicate architecture.

Too Much Risk

Unpatched, outdated systems are prime targets for packaged attacks that move at machine speed. Defense requires recovery tools that teams don’t have.

Enabling businesses to be resilient in the modern age requires an approach that’s different than simply deploying a serial console for remote troubleshooting. Gen 1 and 2 serial consoles, which have dominated the market for 20 years, were designed to solve basic issues by offering limited remote access and some automation. The problem is, these still leave teams lacking the confidence to answer questions like:

  • “How can we guarantee access to fix stuff that breaks, without rolling trucks?”
  • “Can we automate change management, without fear of breaking the network?”
  • “Attacks are inevitable — How do we stop hackers from cutting off our access?”

Hyperscalers, Internet Service Providers, Big Tech, and even the military have a resilience model that they’ve proven over the last decade. Their approach involves fully isolating command and control from data and user environments. This allows them to not only gain low-level remote access to maintain and fix systems, but also to “defend the hill” and maintain control if systems are compromised or destroyed.

This approach uses something called Isolated Management Infrastructure (IMI).

Isolated Management Infrastructure is the best practice for network resilience

Isolated Management Infrastructure is the practice of creating a management network that is completely separate from the production network. Most IT teams are familiar with out-of-band management as this network; IMI, however, provides many capabilities that can’t be hosted on a traditional serial console or OOB network. And with increasing vulnerabilities, CISA issued a binding directive specifically calling for organizations to implement IMI.

Isolated Management Infrastructure using Gen 3 serial consoles, like ZPE Systems’ Nodegrid devices, provides more than simple remote access and automation. Similar to a proper out-of-band network, IMI is completely isolated from production assets. This means there are no dependencies on production devices or connections, and management interfaces are not exposed to the internet or production gear. In the event of an outage or attack, teams retain management access, and this is just the beginning of the benefits of having IMI.

A network architecture diagram showing Isolated Management Infrastructure next to production infrastructure

IMI includes more than nine functions that are required for teams to fully service their production assets. These include:

  • Low-level access to all management interfaces, including serial, Ethernet, USB, IPMI, and others, to guarantee remote access to the entire environment
  • Open, edge-native automation to ensure services can continue operating in the event of outages or change errors
  • Computing, storage, and jumpbox capabilities that can natively host the apps and tools to deploy an IRE, to ensure fast, effective recovery from attacks

Get the guide to build IMI

ZPE Systems has worked alongside Big Tech to fulfill their requirements for IMI. In doing so, we created the Network Automation blueprint as a technical guide to help any organization build their own Isolated Management Infrastructure. Download the blueprint now to get started.

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Edge Computing Requirements

by | Jan 18, 2024 | Consolidation, Edge Computing, Increase Productivity, Network Automation, Network Edge Orchestration, Remote Network Management, Security Service Edge (SSE), Simplify Branch Infrastructure, Vendor Neutral Platform, Zero Touch Provisioning (ZTP)

Edge computing requirements displayed in a digital interface wheel.

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

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

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

Edge computing requirements

The primary requirements for edge computing are:

7 Edge Computing Requirements
    1. Compute (processing, memory, and storage)
    2. Small, ruggedized chassis
    3. Power
    4. Wired & wireless connectivity
    5. Out-of-band (OOB) management
    6. Security
    7. Visibility

1. Compute

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

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

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

2. Small, ruggedized chassis

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

3. Power

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

4. Wired & wireless connectivity

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

5. Out-of-band (OOB) management

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

6. Security

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

7. Visibility

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

Edge Computing Learning Center

Streamlining edge computing requirements

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

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

To learn more about consolidating your edge computing requirements with the vendor-neutral Nodegrid platform, schedule a free demo!

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