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Kubernetes in the wild report 2023

Dynatrace

On-premises data centers invest in higher capacity servers since they provide more flexibility in the long run, while the procurement price of hardware is only one of many cost factors. Specifically, they provide asynchronous communications within microservices architectures and high-throughput distributed systems.

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Towards a Reliable Device Management Platform

The Netflix TechBlog

Complementing the hardware is the software on the RAE and in the cloud, and bridging the software on both ends is a bi-directional control plane. System Setup Architecture The following diagram summarizes the architecture description: Figure 1: Event-sourcing architecture of the Device Management Platform.

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AI for everyone - How companies can benefit from the advance of machine learning

All Things Distributed

This has allowed for more research, which has resulted in reaching the "critical mass" in knowledge that is needed to kick off an exponential growth in the development of new algorithms and architectures. We may have come a relatively long way with AI, but the progress came quietly.

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Dutch Enterprises and The Cloud

All Things Distributed

In addition, its robust architecture supports ten times as many scientists, all working simultaneously. When Tom Tom launched the LBS platform they wanted the ability to reach millions of developers all around the world without having them invest a lot of capital upfront in hardware and building expensive data centers so turned to the cloud.

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Snap: a microkernel approach to host networking

The Morning Paper

I’m jumping ahead a bit here, but the component of Snap which provides the transport and communications stack is called Pony Express. Rather than reimplement TCP/IP or refactor an existing transport, we started Pony Express from scratch to innovate on more efficient interfaces, architecture, and protocol. Enter Google!

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Why I hate MPI (from a performance analysis perspective)

John McCalpin

According to Dr. Bandwidth, performance analysis has two recurring themes: How fast should this code (or “simple” variations on this code) run on this hardware? The user environment defines the mapping of MPI ranks to hardware resources (cores, sockets, nodes). The MPI runtime library. in ways that are seldom transparent.

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RPCValet: NI-driven tail-aware balancing of µs-scale RPCs

The Morning Paper

At one extreme we have a ‘16 x 1’ architecture with 16 queues each with one associated processing unit. At the other extreme is a ‘1 x 16’ architecture with one shared queue serving all 16 processing units. It’s designed for “ emerging architectures featuring fully integrated NIs and hardware-terminated transport protocols.”

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