Petite SRE：An Observation on Cloud Infrastructure Design/Delivery in GenAI Era

As Generative AI applications evolve rapidly, infrastructure design and delivery must keep pace while maintaining reliability and scalability. This paper proposes an integrated modeling approach that places Site Reliability Engineering (SRE) at its core, enabling automated, agile, and observable infrastructure development. Through analysis of current research and industry practices, combined with a real-world case study, we demonstrate how SRE principles can accelerate infrastructure delivery while ensuring long-term sustainability for GenAI workloads.

One of the most important AI-related research papers, the Artificial Intelligence Index 2024, provides a comprehensive overview of the current state of AI, covering various aspects such as research and development, technical performance, design theory, and industry practices. Here are the key highlights:

As everything has cons and pros, some researchers have begun to rethink Generative AI and provide some Frameworks as a reference for practice. As a result, the following framework and key points are presented.

Due to the omnipresence of Generative AI, the blue sea after the above reflection is also obvious.

Agentic AI represents a new generation of increasingly powerful foundation models that act as operating systems for autonomous, action-taking, digital agents capable of enhanced reasoning and decision-making, as well as increasingly disruptive chatbots and copilots. It can be a positive expectation that Generative AI will play a main role in consumer & enterprise task adoption shortly and software's AI moment also coming soon. Cheaper and safer AI tools will come to daily life more as they are aligned with capital-intensive foundation models.

The above research paper and industry report conclusions give the public a fact-based overview of Generative AI. AWS AI Day, on the other hand, demonstrates how leading technology companies are implementing GenAI solutions, highlighting three critical areas for infrastructure architects:

As the clients' IT environment transforms so fast, advanced services must be provided with a delivery-assisted/ requirements-driven approach based on data-driven operations. This makes SRE come to the center when thinking of Infrastructure/Cloud design in the Generative AI Era.

On the other hand, the issue is obvious: how to integrate AI with SRE into project delivery in the Generative AI Era in a reproducible way no matter if the system infrastructure is cloud-native or hybrid, and how to set up an effective professional team/ process to support it.

Our proposed model focuses on 5 interconnected categories, each implemented through SRE's core principles of automation, agility, and observability. This model focused on how to prepare and set up a valid infrastructure that supports AI applications. Integrated into a real-world AWS hybrid case are as follows.

This is the first category that should be discussed before anything else. The final goal, overall strategy, roadmap, and solution should be deeply communicated with all stakeholders and make sure every key performer is at least on the same page with the decisions at a high level. Besides 7R methodologies, commonly used discussion layering and structured strategies for this stage are listed below. These work like a bridge between high-level theory and detailed designs.

After categorizing all target systems, splitting them into groups to fit specific the roadmap discussed from multiple angles, such as cost, time, governance, etc.

This is the second category that should be discussed, and it is also where the SRE cores, automation, agility, and observability, come to the front. Leveraging the decisions into standard design/delivery and tailored design/delivery and applying them to components at a high level. Make sure every function/component of the decided architecture has its layers settled.

The standard design/delivery is the foundation and initial component of Advanced Infrastructure Delivery. It is focused on setting up the basic functions such as networking, computing, and load balancing as fast as possible. They are pre-designed/built IaC modules after several large-scale projects across multiple industries. The general lineup should include the following.

As the previous category brings SRE cores to the stage, this category is the real engine to keep them alive. The time and human resources saved by using standard design/delivery modules can be invested in specified functions/components development tailored based on project needs. To make infrastructure delivery have compatible speed align with application development, 3 things should be done as a precondition.

The basic logic of adapting the above categories lean on SRE cores for infrastructure delivery is the same as the data science circle: collecting source data, data processing, modeling, deploying, monitoring, and analysis/reporting. Every module and people in these processes are data providers and users as they are all under monitoring from the start. This makes the situation a natural continuous input/output machine. To make it more efficient, the following are the key points:

While project members work with passion and eagerness for improvement, it is crucial for leaders to keep portfolio management lean and well-discussed.

The successful delivery of advanced infrastructure in the GenAI Era requires a balanced approach combining SRE principles, clear and practical modeling frameworks that come from real-life business practices, and continuous improvement cycles. By maintaining focus on automation, agility, and observability, organizations can build and maintain infrastructure that evolves with their AI applications while ensuring reliability and scalability.

Although AI services provided by Cloud vendors are implemented as assistants for advanced infrastructure, real AI infrastructure still has a long way to go. Key differentiators from the current cloud infrastructure are emergent intelligence capabilities, self-organizing systems, minimal human intervention, and advanced meta-learning frameworks. To make it closer, the following future tasks need to be done: