Generative AI for IaC and Automating IaC

Current State of Infrastructure as Code (IaC)  

IaC treats physical network, infrastructure, compute like software. This means provisioning and managing the cloud resources happen through code, bringing automation, precision, and repeatability to the process. IaC streamlines management by: 

• Minimizing Human Error: Manual configuration is error prone. IaC scripts ensure consistency and eliminate the risk of human mistakes, leading to a more reliable and predictable network. 

• Enhancing Reproducibility: IaC scripts allow for easy replication of network configurations, facilitating rapid deployments and disaster recovery. This allows for faster rollouts of new services and quicker response times in case of outages. 

• Streamlining Scaling: IaC simplifies scaling network resources to meet changing demands. As network traffic fluctuates, infrastructure can be easily scaled up or down, ensuring optimal resource utilization. 

Using Generative AI IaC  

 Fig: Generative AI In IaC 

While IaC offers numerous advantages, crafting these scripts manually can be time-consuming and complex. SRE engineers often need to possess specialized skillsets to write efficient and compliant IaC code. This is where Generative AI steps in, revolutionizing the IaC development process. 

Benefits:

• Effortless Script Generation: LLMs can analyse complex network requirements and architecture diagrams. Based on this understanding, they can automatically generate accurate, efficient, and compliant IaC scripts. This frees up valuable time for network engineers, allowing them to focus on more strategic tasks like network design and optimization. 

• Reduced Errors: Generative AI tools enhance accuracy by maintaining consistency and following best practices in Infrastructure as Code (IaC) development, thereby reducing the likelihood of configuration errors that may result in network outages or security vulnerabilities.

Advantages of Generative AI in IaC

The adoption of Generative AI based IaC offers a multitude of advantages for teams using Cloud Infrastructure. 

• Faster Development Cycles: Generating IaC through LLM Code Generation for provisioning and network infrastructure management significantly reduces development time, allowing operators to bring new services to market faster. This translates to quicker time-to-revenue and a competitive edge in a rapidly evolving market. 

• Reduced Operational Costs: IaC through LLM minimizes manual intervention, leading to lower operational costs associated with network management. Less time spent on manual configuration equates to reduced labour costs and the ability to optimize resource allocation. 

• Improved Network Efficiency: AI-powered network optimization ensures efficient resource utilization and optimal network performance. LLMs for IaC Code Generation can continuously analysed network traffic patterns and automatically adjust configurations for optimal performance, leading to a more reliable and responsive network experience for end users.  

Fig: Working of Infrastructure as Code (IaC) 

Case Study - Large Telecom Provider Streamlines Network Rollout with IaC 

The telecommunications industry is evolving and accepting modern technologies in the services. Traditional, hardware-centric networks are giving way to the agility and power of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). This shift unlocks a treasure of benefits for telecom operators, including: 

• Unparalleled Flexibility: SDN provides on-the-fly network configuration. It utilizes software, enabling swift adaptation to changing demands. 

• Enhanced Efficiency: NFV virtualizes network functions, maximizing resource utilization and reducing hardware dependency. 

• Reduced Operational Costs: Streamlined networks and automation lead to significant cost savings. 

A major telecommunications company needed to rapidly deploy a new network service across multiple regions. Manual configuration of network devices was time-consuming and prone to errors. 

To overcome these issues the company implemented an IaC solution using tools like Terraform. They created Infrastructure as Code (IaC) scripts to automate the setup of network devices such as routers, switches, and firewalls.

Benefits: 

• Faster Deployments: IaC automation drastically reduced deployment times compared to manual configuration. 

• Reduced Errors: Automated scripting minimized configuration errors, leading to a more reliable and predictable network. 

• Improved Consistency: IaC ensured consistent configurations across all deployed network elements. 

Adoption of Generative AI in IaC  

The most convenient option currently in adoption of IaC is to invest in LLM that is trained on Programming Languages and Frameworks. If the Service or the Model itself supports APIs and integrates well in your pipeline, it should be your first step in Modernising the IaC. 

Use Existing Generative AI Tools  

1. OpenAI Codex 

OpenAI Codex, based on GPT-3, is a large language model trained on diverse code sources. It can generate code snippets in multiple programming languages based on natural language descriptions. It is integrated into various development environments and tools. 

2. GitHub CoPilot 

GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It is built on the GPT-3 language model and is designed to assist developers in writing code more efficiently by providing context-aware code suggestions directly within their integrated development environment (IDE). It can be used to Generate IaC 

3. Devin AI  

Devin AI claims itself as AI Software Engineer and Capable to write IaC that can deploy into limited Cloud Provider currently, but it promises to deploy on all leading cloud provider in future. 

4. Claude 2  

Claude 2 can read and analyse popular programming languages, including Python, JavaScript, SQL, and CSS. This capability, combined with its impressive input capacity of up to 100k tokens, allows users to upload their entire code for debugging. It has capability to write IaC in popular Frameworks too 

Limitations 

While it is convenient to opt for this option, Enterprises may have to consider going through terms and conditions, Privacy Policies and Legalities before opting for these services in their enterprise. Since these services improves further by Reinforcement Learning and Training on your Prompts and its outputs, there are chances that the Models learn your Proprietary code and spits out a Confidential algorithm to other customers. Hence, LLM Security is an active field of research. If you genuinely want to use LLM, it is worthily to invest on something foundational and then build on top of that. 

Unleash the Real Value & Build Your Own Agent  

Current State of Internal Developer Platforms are not using AI but integrating Software Delivery and Supply Chain in more automation, Giving Seamless experience to Developers to start a Project using Fixed Project Templates and with all the Toolchains Integrated. 

• Your Enterprise's Code should remain private and building your own Generative Agents by using existing ones keeps your Enterprise’s algorithms and Code Private. 

• Fine Tune your Generative Agent’s according to your enterprise’s ecosystem as every enterprise is unique.  

Future of a Developer Platform & Platform Engineering Revolves around Generative AI agents for IaC 

Future of Infrastructure as a Code 

The table provides a comparison of Infrastructure as Code (IaC) across past, present, and future states, focusing on various aspects such as tracking state of deployed infrastructure, IaC drift, coverage, test cases, maintainability, and human intervention. 

In the past, IaC was characterized by bunch of automation frameworks sitting here and there in an organisation. In the present, there has been an evolution towards infrastructure tracking, moving towards more narrowed and measured IaC drift. It is done to measure how much infrastructure is uncoordinated of IaC. There has been emphasis on larger coverage of IaC in Cloud Environment. Adoption of Standard are leading less painful maintenance, and a transition towards zero-touch deployments albeit with some difficulty. 

In the future, the trend is towards no IaC drift, full coverage of IaC as the Cloud Infrastructure is fully managed through IaC code generated through IaC and there is bidirectional Sync. Availability of full test cases, maintenance-free infrastructure, and zero-touch deployments facilitated by advanced technologies such as General AI (Gen AI), reducing the need for human intervention in deployment processes.

• Read more about Infrastructure as Code in CI/CD Pipeline

• Know the IaC Principles for Kubernetes Configuration Management.