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

Stefan Dietrich

The challenge today for network operations (NetOps) is how to maintain and evolve the network while demand for network services continues to grow. Software-Defined Networking (SDN) promises to make the network more agile and adaptable. Various solutions exist, yet most are missing a layer to orchestrate new features and policies in a standardized, automated and replicable manner while providing sufficient customization to meet enterprise-level requirements.

NetOps is often working with wide area networks ("WANs") that are geographically diverse, use a plethora of technologies from different services providers and are feeling the strain from increasing use of video and cloud application services. Hybrid WAN architectures with advanced application-level traffic routing are of particular interest. They combine the reliability of private lines for critical business applications with the cost-effectiveness of broadband/Internet connectivity for non-critical traffic.

Here's the issue: many of the network management tools available today are insufficient to deploy such architectures at scale over the existing network. Most of them still apply blocks of configuration data to network devices to enable features that in turn enable an overall network policy. To allow adjustment of configuration data to address differences in hardware and OS/firmware levels, those scripts are using "wildcards" replacing certain configuration data. These scripts are heavily tested, carefully curated and subject to stringent change management procedures. The tiniest mistake can bring a network down, resulting in potentially disastrous business losses.

NetOps teams are seeing first-hand how inadequate this approach is. As they deploy hybrid WAN architectures and application-specific routing, network operations teams are experiencing the limits to this approach. Even if the existing hardware already supports all the functionality required, existing network configurations that reflect past user requirements are rarely well understood. As each business unit is asking for specific requirements to ensure that their applications run optimally on the network, networks need to be continuously updated and optimized. Such tasks range from a simple adjustment of the configuration parameters to more complex changes of the underlying network architecture, such as removing and installing upgraded circuits, replacing hardware or even deploying new network architectures.

In these instances, senior network architects must be heavily relied upon to determine potential risk of unintentional consequences on the existing network, but waiting for the next change maintenance window may no longer be an acceptable option. Businesses are not concerned with the details; they want the networks to simply "work."

Moving Forward: the Ideal vs. the Real

What needs to happen in order for the network to simply work? Traditional network management tools are mature and well understood. Network architects and implementation teams are familiar with them, including all of the limitations and difficulties, and any potential change of these tools is immediately vetted against the additional learning curve required vis-à-vis potential benefits in managing the network.

An ideal situation would be one in which the network policies are defined independently of implementation or operational concerns. It starts with mapping of the required functionality into a logical model, assembling these models into one overall network policy, verifying interdependencies and inconsistencies, and deploying and maintaining them consistently throughout the network life cycle.

The current situation is less than ideal, though. The industry has launched a variety of activities to improve network management, but those initiatives are still maturing. For example, YANG is a data modeling language for the NETCONF network configuration protocol. OpenStack Networking (Neutron) is providing an extensible framework to manage networks and IP addresses within the larger realm of cloud computing, focusing on network services such as intrusion detection systems (IDS), load balancing, firewalls and virtual private networks (VPN) to enable multi-tenancy and massive scalability. But neither approach can proactively detect interdependencies or inconsistencies, and both require network engineers to dive into programming, for example, to manage data entry and storage.

It makes sense, then, that some vendors are offering fully integrated solutions, built on appliances managed through a proprietary network management tool. This model allows businesses to deploy solutions quickly, at the cost of additional training, limited capability for customization and new hardware purchases.

In order for transformation to occur, the focus of new network management capabilities needs to be on assembling complete network policies from individual device-specific features, detecting inconsistencies and dependencies, and allowing deployment and ongoing network management. Simply updating wildcards in custom configuration templates and deploying them onto devices is no longer sufficient.

As needs and technologies shift and evolve, network architectures or routing protocol changes may need to be changed on live production networks. Managing such changes at large scale is difficult or even infeasible. This is especially true in large organizations where any change will always have to be validated by e.g. security. This creates unacceptable delays for implementation.

To find out more about solving these network operations challenges, read Best Practices for Modeling and Managing Today's Network - Part 2

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

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

Stefan Dietrich

The challenge today for network operations (NetOps) is how to maintain and evolve the network while demand for network services continues to grow. Software-Defined Networking (SDN) promises to make the network more agile and adaptable. Various solutions exist, yet most are missing a layer to orchestrate new features and policies in a standardized, automated and replicable manner while providing sufficient customization to meet enterprise-level requirements.

NetOps is often working with wide area networks ("WANs") that are geographically diverse, use a plethora of technologies from different services providers and are feeling the strain from increasing use of video and cloud application services. Hybrid WAN architectures with advanced application-level traffic routing are of particular interest. They combine the reliability of private lines for critical business applications with the cost-effectiveness of broadband/Internet connectivity for non-critical traffic.

Here's the issue: many of the network management tools available today are insufficient to deploy such architectures at scale over the existing network. Most of them still apply blocks of configuration data to network devices to enable features that in turn enable an overall network policy. To allow adjustment of configuration data to address differences in hardware and OS/firmware levels, those scripts are using "wildcards" replacing certain configuration data. These scripts are heavily tested, carefully curated and subject to stringent change management procedures. The tiniest mistake can bring a network down, resulting in potentially disastrous business losses.

NetOps teams are seeing first-hand how inadequate this approach is. As they deploy hybrid WAN architectures and application-specific routing, network operations teams are experiencing the limits to this approach. Even if the existing hardware already supports all the functionality required, existing network configurations that reflect past user requirements are rarely well understood. As each business unit is asking for specific requirements to ensure that their applications run optimally on the network, networks need to be continuously updated and optimized. Such tasks range from a simple adjustment of the configuration parameters to more complex changes of the underlying network architecture, such as removing and installing upgraded circuits, replacing hardware or even deploying new network architectures.

In these instances, senior network architects must be heavily relied upon to determine potential risk of unintentional consequences on the existing network, but waiting for the next change maintenance window may no longer be an acceptable option. Businesses are not concerned with the details; they want the networks to simply "work."

Moving Forward: the Ideal vs. the Real

What needs to happen in order for the network to simply work? Traditional network management tools are mature and well understood. Network architects and implementation teams are familiar with them, including all of the limitations and difficulties, and any potential change of these tools is immediately vetted against the additional learning curve required vis-à-vis potential benefits in managing the network.

An ideal situation would be one in which the network policies are defined independently of implementation or operational concerns. It starts with mapping of the required functionality into a logical model, assembling these models into one overall network policy, verifying interdependencies and inconsistencies, and deploying and maintaining them consistently throughout the network life cycle.

The current situation is less than ideal, though. The industry has launched a variety of activities to improve network management, but those initiatives are still maturing. For example, YANG is a data modeling language for the NETCONF network configuration protocol. OpenStack Networking (Neutron) is providing an extensible framework to manage networks and IP addresses within the larger realm of cloud computing, focusing on network services such as intrusion detection systems (IDS), load balancing, firewalls and virtual private networks (VPN) to enable multi-tenancy and massive scalability. But neither approach can proactively detect interdependencies or inconsistencies, and both require network engineers to dive into programming, for example, to manage data entry and storage.

It makes sense, then, that some vendors are offering fully integrated solutions, built on appliances managed through a proprietary network management tool. This model allows businesses to deploy solutions quickly, at the cost of additional training, limited capability for customization and new hardware purchases.

In order for transformation to occur, the focus of new network management capabilities needs to be on assembling complete network policies from individual device-specific features, detecting inconsistencies and dependencies, and allowing deployment and ongoing network management. Simply updating wildcards in custom configuration templates and deploying them onto devices is no longer sufficient.

As needs and technologies shift and evolve, network architectures or routing protocol changes may need to be changed on live production networks. Managing such changes at large scale is difficult or even infeasible. This is especially true in large organizations where any change will always have to be validated by e.g. security. This creates unacceptable delays for implementation.

To find out more about solving these network operations challenges, read Best Practices for Modeling and Managing Today's Network - Part 2

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 ...