[7/7] Meta's Hyperscale Infrastructure: Overview and Insights (Category: Infrastructure)
[7/7] Meta's Hyperscale Infrastructure: Overview and Insights (Rubric #Infrastructure)
This post is the final in the consideration of a cool review article from the banned in Russia company Meta (previous issues: 1, 2, 3, 4, 5 and 6). Here we will discuss how the author sees the further development of infrastructure, architecture and AI penetration into the company’s systems. I note that this part was very interesting to me - to put together a puzzle about how history developed is one thing, and to make a qualitative prediction is already a problem with an asterisk.
AI and the New Data Center Architecture AI workloads have already become the main consumer of Meta resources: by the end of the decade, they will occupy more than half of the data center’s capacity. Unlike classic web services, model learning requires hundreds of terabytes of data, powerful GPUs, and ultrafast networks. This leads to a paradigm shift from scale-out. (cheap-knot) To scale-up, when large AI clusters are created that resemble supercomputers. Meta builds a full stack for AI: from PyTorch and models to its own chips (MTIA)Network solutions, storage and cooling systems. Everything is designed in a complex way to work synchronously. In the future, data centers will become half-machines for learning AI, which will change their entire architecture.
The era of specialized iron After the era of server unification, the reverse process begins: the rise of custom ASICs and accelerators. Hyperscalers can afford to design their own chips for AI training, compression, encryption, video encoding, In-Network/In-Storage-Processing, etc. Meta expects data centers to evolve into heterogeneous clusters of many types of hardware. The main challenge is to teach software to effectively use such diverse resources. This will require new levels of abstraction and orchestration. But the gains in energy efficiency and cost on millions of servers will pay off the effort.
Regional Data Centers and the Metaverse Meta predicts rapid growth of infrastructure on the "edge" of the network - mini-data centers close to users. This is necessary for AR/VR, cloud gaming and IoT, where delay is critical.25 Ms. The company is building a Global Data-center-as-a-Computer model: applications will automatically run where the user is closer, without the developer’s input. The architecture will become multi-level - large regions + a network of micro-data centers united by a common orchestration system.
Breakthrough in development tools Meta expects a qualitative leap in productivity of engineers due to two factors
- Mass implementation of AI assistants (Copilot, GPT-4 etc.), which automate code generation, bug search and refactoring and so on
- The emergence of vertically integrated platforms, where the developer describes only business logic, and the infrastructure is hidden under the hood. An example is the internal FrontFaaS project, which accelerates the creation of web interfaces. Similar frameworks will appear in other domains, radically increasing individual productivity.
Joint development The author emphasizes: 20 For years, hyperscalers have set the pace for the entire industry, and AI will only accelerate this process. In order for innovation to spread faster, we need to share experiences. Meta encourages the publication of open source projects and research, as it does itself. This article serves exactly that purpose: to show what bricks Meta’s infrastructure is built from and what principles can inspire engineers around the world.
This is a really good article from Meta that was interesting to read. In the future, I plan to find and disassemble similar articles from other companies.
#Infrastructure #PlatformEngineering #Architecture #DistributedSystems #SystemDesign #Engineering #Software #DevEx #DevOps