Remove Google Remove Hardware Remove Tuning
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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

VMware commercialized the idea of virtual machines, and cloud providers embraced the same concept with services like Amazon EC2, Google Compute, and Azure virtual machines. Performing updates, installing software, and resolving hardware issues requires up to 17 hours of developer time every week.

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Kubernetes vs Docker: What’s the difference?

Dynatrace

Container technology is very powerful as small teams can develop and package their application on laptops and then deploy it anywhere into staging or production environments without having to worry about dependencies, configurations, OS, hardware, and so on. The time and effort saved with testing and deployment are a game-changer for DevOps.

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An analysis of performance evolution of Linux’s core operations

The Morning Paper

Perhaps the most interesting lesson/reminder is this: it takes a lot of effort to tune a Linux kernel. Google’s data center kernel is carefully performance tuned for their workloads. On the exact same hardware, the benchmark suite is then used to test 36 Linux release versions from 3.0 Measuring the kernel.

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Generative AI in the Enterprise

O'Reilly

Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking. That pricing won’t be sustainable, particularly as hardware shortages drive up the cost of building infrastructure.

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AI’s Future: Not Always Bigger

O'Reilly

These smaller distilled models can run on off-the-shelf hardware without expensive GPUs. And they can do useful work, particularly if fine-tuned for a specific application domain. Amazon Web Services, Microsoft Azure, Google Cloud, and many smaller competitors offer hosting for AI applications.

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Software-defined far memory in warehouse scale computers

The Morning Paper

This makes memory a critical factor in the total cost of ownership (TCO) of large compute clusters, or as Google like to call them “Warehouse-scale computers (WSCs).” ” This paper describes a “far memory” system that has been in production deployment at Google since 2016. Enter zswap!

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Snap: a microkernel approach to host networking

The Morning Paper

This paper describes the networking stack, Snap , that has been running in production at Google for the last three years+. Enter Google! The ability to rapidly deploy new versions of Pony Express significantly aided development and tuning of congestion control. Snap: a microkernel approach to host networking Marty et al.,

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