How network data models work with automation

Types of network data models

Network data models provide several advantages in networking, such as standardization, abstraction, automation, interoperability and policy enforcement. These models play a critical role in modern networking by providing a common language and framework to describe, manipulate, and automate network resources and services.

1. Physical network model

This model represents the physical components of a network, such as switches, network interfaces, bridges, cables and hubs. It describes their physical connections and attributes.

Relationship to automation: Network automation relies heavily on the physical network model to provide essential information about the underlying infrastructure. This model enables automation tools and scripts to do the following tasks:

• Gather inventory information.
• Provision physical network resources.
• Verify hardware capabilities.
• Perform device discovery.

2. Logical network model

The logical network model simplifies the physical infrastructure and defines logical relationships, addressing schemes and routing domains, independent of the underlying hardware.

Relationship to automation: Network automation uses the logical network model to define network policies, configurations and topologies in a vendor-agnostic manner. Network engineers can use automation libraries, such as NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support), to work in multivendor environments without worrying about proper command syntax for each vendor. Python scripts and orchestration workflows can use this logical model to deploy consistent network configurations, manage virtualized network functions, and ensure network scalability and agility.

3. Protocol data model

This model specifies the structure, semantics and behavior of network protocols, such as Border Gateway Protocol, Open Shortest Path First and TCP/IP. It also defines the formatting of protocol messages, protocol states and their interactions.

Relationship to automation: Networking and its underlying protocols are essential components of automation. Automation tools rely on protocol data models to interact with network devices and protocols programmatically. Numerous automation tools are currently available in the industry. These tools use protocol-specific APIs, libraries and data structures to communicate with devices, exchange control messages and enforce protocol configurations.

Network engineers should understand protocol data models to implement automation tasks, such as protocol configuration, monitoring and troubleshooting.

4. Network traffic model

Network traffic flow within a network is characterized by its behavior, traffic patterns and bandwidth usage. This model also includes metrics such as packet loss, latency and throughput.

Relationship to automation: Network automation uses the network traffic model to optimize network performance, capacity planning and traffic engineering. Automation tools collect and analyze traffic data from network devices, flow collectors and monitoring systems. They use this data to make informed decisions about traffic routing, load balancing and quality of service (QoS) policies. By analyzing the traffic data, automation tools can optimize network performance and ensure efficient traffic distribution, resulting in seamless UX.

5. Security policy model

The security policy model defines the rules, permissions and restrictions that govern network security policies. These include access control, firewall rules, encryption settings and threat detection mechanisms. This model plays a crucial role in the network.

Relationship to automation: Network automation can integrate a security policy model to automate security policy enforcement, compliance auditing and threat response. This automation is made possible by using scripts, automation and security tools to do the following:

• Dynamically configure security policies.
• Detect security incidents.
• Orchestrate remediation actions across the network infrastructure.

6. QoS model

The QoS model establishes guidelines and protocols for managing and prioritizing network traffic. These guidelines are based on specific service levels, performance objectives and application requirements.

Relationship to automation: Network automation uses the QoS model to automate traffic classification, congestion control and provisioning tasks. Automation tools can help adjust QoS settings dynamically, allocate bandwidth resources and enforce traffic prioritization policies to optimize network performance. These settings ensure that critical applications receive the necessary priority and bandwidth, resulting in a better quality of experience for users.

Network automation best practices

Automation is essential in all network models, as it simplifies repetitive tasks, ensures consistency and dynamically responds to network changes. By using automation tools and adopting best practices, organizations can streamline network operations, enhance efficiency, and improve overall network performance and security.

Below are some best practices to automate workflows:

• Implement infrastructure as code.
• Adopt software-defined networking.
• Standardize protocol configurations.
• Deploy traffic analysis tools.
• Implement policy automation.
• Define clear QoS policies.

Organizations can improve network performance and security by implementing automation in network models to streamline operations and enhance efficiency.

Verlaine Muhungu is a self-taught freelance network technician. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.