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How to build a virtual network lab in the AI era
Network teams use virtual labs to test new network configurations and technologies. As AI integrates into networks, virtual labs must upgrade to handle their massive workloads.
AI can't exist without network engineering.
Everything in today's world runs on the network; it's the backbone of the digital economy. However, the network can't remain stagnant -- it must incorporate new technologies such as AI to remain functional. Network engineers must be active contributors, not just spectators of industry change. Given the pace at which AI evolves, those who don't continuously learn will be left behind.
Not everyone can afford expensive hardware for daily training. Building a virtual lab, then, is crucial for skill upkeep. Simulation and emulation tools enable network engineers to practice, upskill and stand out in the highly competitive tech industry.
Future-ready networking environments require specialized virtual labs to fully harness AI's capabilities. These labs must offer enhanced hardware, dynamic topologies and AI-driven testing. By implementing these strategies, network professionals can effectively include AI in their infrastructure.
Virtual lab capabilities in the AI era
Virtual labs provide an excellent way for network administrators to simulate various scenarios. This enables them to view how different conditions can influence the network, especially as AI integration deepens in the network. However, as networks grow more complex, virtual labs haven't kept pace with advancements. The range of complex features virtual labs can support is currently limited.
Supported capabilities
Virtual network labs can support some AI or AI-adjacent capabilities. They enable several capabilities, including the following:
- Network automation. Handles basic network automation, such as scripted configurations and rule-based policies.
- Traffic simulation. Simulates network traffic, but only with limited-scale synthetic data generation.
- Network performance monitoring. Monitors network performance, including latency and throughput metrics, in standard labs.
Unsupported capabilities
However, despite their benefits, virtual network labs are difficult to use in the AI era. They can't keep up with AI's rapid innovation velocity and the variety of available technologies. They are limited by technical constraints and generally lack built-in support for AI and data-intensive workloads.
Drawbacks of virtual network labs include the following:
- Inability to emulate AI-driven traffic. Simulating complex and dynamic traffic patterns generated by AI applications is beyond the scope of most traditional labs.
- Absence of AI-specific tools. Frameworks such as CUDA, PyTorch or TensorFlow lack data labeling and model training tools.
- Limited data processing. Virtual labs often lack computational power and storage capacity, so they can't handle the massive datasets required to train AI models.
- AI model training. Real-time AI/ML model training requires GPU acceleration, which virtual labs don't typically have.
- Data generation. Virtual labs can't handle large-scale synthetic data generation, which needs high-performance storage.
- Dynamic topology adaptation. Dynamic topology adaptation for AI-driven network reconfiguration, which optimizes the network according to real-time conditions, isn't supported in virtual labs.
Redefine the lab architecture
Virtual lab architectures must adapt to include AI capabilities. Network administrators should incorporate specialized hardware and flexible, dynamic topologies.
Some specialized hardware that AI virtual labs require includes virtual GPU integration and high-speed networking capabilities. Virtual GPU integration enables computational power allocation to specific virtual machines. Meanwhile, high-speed networking -- such as 100 Gbps Ethernet -- interfaces to connect compute and storage, preventing data transfer bottlenecks.
Topology considerations include the following:
- Dynamic topology. The lab must facilitate on-demand network topology creation and modification. This capability enables rapid construction and dismantling of various network scenarios for training and testing.
- Containerization. Topologies that implement containers and orchestration tools such as Docker and Kubernetes help isolate and manage AI workloads. This prevents interference with other lab functions.
- Integration with public cloud. A hybrid architecture that integrates with public cloud providers -- such as AWS, Microsoft Azure and Google Cloud -- can provide access to specialized AI services and scalable resources that are too costly to maintain on-premises.
Evolve testing processes
The shift to an network lab with AI also requires a new approach to testing. AI-driven networks constantly learn and adapt. This necessitates a move toward continuous integration and continuous delivery pipelines for network changes. CI/CD pipelines enable automated and continuous testing for new models and configurations.
A significant part of the testing process now involves validating the training data's quality and integrity. Poor data leads to poor model performance, so a focus on data validation is essential.
Testing should go beyond the network on which the models operate. Develop specific tests to evaluate the AI model's performance. This includes testing for accuracy, bias and stability under different conditions.
How to build an AI virtual lab
Use the following steps to build an AI virtual network lab.
- Define use cases. Identify AI applications such as correlation and root cause analysis, as well as AI-augmented troubleshooting.
- Select tools. To build virtual networks, use simulation and emulation platforms such as Cisco Modeling Labs, EVE-NG or Graphical Network Simulator-3.
- Provision hardware. Ensure the virtual lab has GPU support and high-speed networking.
- Integrate AI frameworks. Deploy AI frameworks, such as CUDA, PyTorch or TensorFlow.
- Test and optimize. Validate AI models in simulated environments before production.
Best practices for AI virtual network labs
A network lab with AI should follow best practices to ensure smooth operation. Consider the following best practices when building a network lab.
- Automation. Automation is key to managing an AI lab's complexity, from environment provisioning to testing and data management. Use automation tools such as Ansible, Terraform and Python scripts.
- Security. Implement strong security measures to protect sensitive data and AI models. This includes access control, data encryption and regular security audits.
- Monitoring. Use advanced monitoring tools to track network and AI workload performance. Look for indicators such as GPU use, data pipeline bottlenecks and model inference latency.
These best practices, along with appropriate architecture and processes, make for effective AI use in a virtual lab. AI is useful for innovating and managing future networks, but network professionals must first embrace these changes.
Verlaine Muhungu is a self-taught tech enthusiast, DevNet advocate and aspiring Cisco Press author, focused on network automation, penetration testing and secure coding practices. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.
