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Best Practices for Modeling and Managing Today's Network - Part 2

Stefan Dietrich

Start with Best Practices for Modeling and Managing Today's Network - Part 1

New Features, New Benefits

Network features and related policies can be mapped using these four constructs:

Domains: Apply configuration settings consistently across multiple devices. An example is a QoS configuration which may be different by business units, hence, different QoS domains would allow network engineers to assign QoS policies across all devices associated with specific business units in each region.

Features: Give the configuration settings for one device at a time, enabling functionality that the device can provide by itself. A good example is the configuration of a device-specific routing table where the device should forward incoming traffic.

Globals: Apply these configuration settings throughout the network; these are the same for every device in the network. A good example is NTP (network time protocol) where the central architecture team is defining the only NTP servers permissible for the network.

Custom: There will always be exceptions, so not everything may be practical to model in a general feature or domain concepts, especially specific exceptions to single devices only. For example, a specific set of Access Control Lists (ACLs) may only be needed on a single device. For these cases where no other dependencies with other features exist, just applying configuration data to a device may be acceptable. 

Whatever network policy is needed can be built using a combination of these constructs. Inherent interdependencies can be flagged by network engineers early, so that a network management system can deploy them in the correct sequential order, optimally applying these features to individual devices as well as across the network to create the target policy. Abstracting network functionality into these types of models allows network engineers to re-focus on the actual network architecture and focus less on the mechanics of the management of configuration data. These lead to a number of benefits:

Any hardware, any manufacturer: How a device is configured is now based on how it should perform, by itself or in concert with other devices. As a result, the actual hardware itself, its specific OS/firmware or even the manufacturer no longer matters, as long as the device is capable of performing the desired functionality.

Logical separation: NetOps is logically separated from implementation and maintenance (DevOps). For example, architects can define the features, domains and global settings needed for a given network infrastructure, assemble them into logical groups and resolve any interdependencies. They can then be tested and validated by, for example, the security team. The assembled features, domains and globals are handed over to the operational team, who will deploy them onto the network and manage them over their lifecycle.

Communal wisdom: When networks are modeled through logical constructs, it allows for a wide exchange of best-practice reference designs based on common user requirements. Different teams of architects can exchange information about the models they use for specific network functionalities without having to revert to low-level configuration settings. This opens the possibility of creating network engineering communities that exchange specific models based on their desired use cases with clearly defined interdependencies and conflict resolution against other models.

Managing the Modern Network

What is needed to create a next-generation network management tool? Nothing less than the development of a sophisticated network-aware orchestration engine that is able to detect any interdependencies, resolve them and deploy network policies automatically over the network.

First, consider these non-technical challenges:

■ Users need to firmly believe that the logical network model will, in fact, result in the correct configuration of all devices in the network. Many network engineers are still most comfortable with command line interface (CLI) created from scripts and templates.

■ The primary focus of network engineers is on proper device configurations and ensuring the device is performing as intended. Any next-generation tools have been designed with a network engineering focus in mind, allowing network engineers to use the system with a much shorter learning curve and minimal programming expertise.

■ Get the buy-in of DevOps and NetOps teams, who may be skeptical to trust device configuration to a new management tool.

Technically speaking, here's what today's management tools should include:

■ Management to handle the high degree of customization needed.

■ Zero-touch provisioning so that the onboarding of new devices into the system is as fluid as possible, allowing generalist IT staff to install routers and trigger device provisioning automatically.

■ The ability to limit or flag unauthorized manual device configuration changes with automatic remediation when needed.

■ Configuration preview that allows dry runs of new configurations to understand all changes that may have to be performed, even on other network devices when needed.

■ Step-by-step verification of device provisioning actions with automatic revert on errors.

A New Approach

Organizations can bring their networks into present-day functionality with tools that provide complete abstraction of network functions while providing deeply integrated model interdependency verification, deployment previews and layer-by-layer provisioning. For example, replacing an existing device with a newer model, even if it's from a different vendor, can be detected and automatically provisioned. Such solutions that can resolve any potential conflicts and interdependencies, even across vendors, are becoming increasingly important as network devices are virtualized on common platforms and the individual strength of vendor-specific solutions are combined into one multi-vendor solution.

A model like this that addresses the entire stack provides clarity to architecture and implementation teams because the handoff points are well defined. This, in turn, leads to faster implementation of business requirements and higher reliability. Such a system creates quicker identification of and recovery from network outages, which increases customer confidence and satisfaction and saves money from unexpected downtime.

Dr. Stefan Dietrich is VP of Product Strategy at Glue Networks.

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The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

Best Practices for Modeling and Managing Today's Network - Part 2

Stefan Dietrich

Start with Best Practices for Modeling and Managing Today's Network - Part 1

New Features, New Benefits

Network features and related policies can be mapped using these four constructs:

Domains: Apply configuration settings consistently across multiple devices. An example is a QoS configuration which may be different by business units, hence, different QoS domains would allow network engineers to assign QoS policies across all devices associated with specific business units in each region.

Features: Give the configuration settings for one device at a time, enabling functionality that the device can provide by itself. A good example is the configuration of a device-specific routing table where the device should forward incoming traffic.

Globals: Apply these configuration settings throughout the network; these are the same for every device in the network. A good example is NTP (network time protocol) where the central architecture team is defining the only NTP servers permissible for the network.

Custom: There will always be exceptions, so not everything may be practical to model in a general feature or domain concepts, especially specific exceptions to single devices only. For example, a specific set of Access Control Lists (ACLs) may only be needed on a single device. For these cases where no other dependencies with other features exist, just applying configuration data to a device may be acceptable. 

Whatever network policy is needed can be built using a combination of these constructs. Inherent interdependencies can be flagged by network engineers early, so that a network management system can deploy them in the correct sequential order, optimally applying these features to individual devices as well as across the network to create the target policy. Abstracting network functionality into these types of models allows network engineers to re-focus on the actual network architecture and focus less on the mechanics of the management of configuration data. These lead to a number of benefits:

Any hardware, any manufacturer: How a device is configured is now based on how it should perform, by itself or in concert with other devices. As a result, the actual hardware itself, its specific OS/firmware or even the manufacturer no longer matters, as long as the device is capable of performing the desired functionality.

Logical separation: NetOps is logically separated from implementation and maintenance (DevOps). For example, architects can define the features, domains and global settings needed for a given network infrastructure, assemble them into logical groups and resolve any interdependencies. They can then be tested and validated by, for example, the security team. The assembled features, domains and globals are handed over to the operational team, who will deploy them onto the network and manage them over their lifecycle.

Communal wisdom: When networks are modeled through logical constructs, it allows for a wide exchange of best-practice reference designs based on common user requirements. Different teams of architects can exchange information about the models they use for specific network functionalities without having to revert to low-level configuration settings. This opens the possibility of creating network engineering communities that exchange specific models based on their desired use cases with clearly defined interdependencies and conflict resolution against other models.

Managing the Modern Network

What is needed to create a next-generation network management tool? Nothing less than the development of a sophisticated network-aware orchestration engine that is able to detect any interdependencies, resolve them and deploy network policies automatically over the network.

First, consider these non-technical challenges:

■ Users need to firmly believe that the logical network model will, in fact, result in the correct configuration of all devices in the network. Many network engineers are still most comfortable with command line interface (CLI) created from scripts and templates.

■ The primary focus of network engineers is on proper device configurations and ensuring the device is performing as intended. Any next-generation tools have been designed with a network engineering focus in mind, allowing network engineers to use the system with a much shorter learning curve and minimal programming expertise.

■ Get the buy-in of DevOps and NetOps teams, who may be skeptical to trust device configuration to a new management tool.

Technically speaking, here's what today's management tools should include:

■ Management to handle the high degree of customization needed.

■ Zero-touch provisioning so that the onboarding of new devices into the system is as fluid as possible, allowing generalist IT staff to install routers and trigger device provisioning automatically.

■ The ability to limit or flag unauthorized manual device configuration changes with automatic remediation when needed.

■ Configuration preview that allows dry runs of new configurations to understand all changes that may have to be performed, even on other network devices when needed.

■ Step-by-step verification of device provisioning actions with automatic revert on errors.

A New Approach

Organizations can bring their networks into present-day functionality with tools that provide complete abstraction of network functions while providing deeply integrated model interdependency verification, deployment previews and layer-by-layer provisioning. For example, replacing an existing device with a newer model, even if it's from a different vendor, can be detected and automatically provisioned. Such solutions that can resolve any potential conflicts and interdependencies, even across vendors, are becoming increasingly important as network devices are virtualized on common platforms and the individual strength of vendor-specific solutions are combined into one multi-vendor solution.

A model like this that addresses the entire stack provides clarity to architecture and implementation teams because the handoff points are well defined. This, in turn, leads to faster implementation of business requirements and higher reliability. Such a system creates quicker identification of and recovery from network outages, which increases customer confidence and satisfaction and saves money from unexpected downtime.

Dr. Stefan Dietrich is VP of Product Strategy at Glue Networks.

Hot Topics

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...