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An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems

The Morning Paper

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems Gan et al., The paper examines the implications of microservices at the hardware, OS and networking stack, cluster management, and application framework levels, as well as the impact of tail latency.

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This spring: High-Performance and Low-Latency C++ (Stockholm) and ACCU (Bristol)

Sutter's Mill

Tue-Thu Apr 25-27: High-Performance and Low-Latency C++ (Stockholm). On April 25-27, I’ll be in Stockholm (Kista) giving a three-day seminar on “High-Performance and Low-Latency C++.”

Latency 51
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What is a Distributed Storage System

Scalegrid

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130
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Achieving 100Gbps intrusion prevention on a single server

The Morning Paper

Options 1 and 2 are of course the ‘scale out’ options, whereas option 3 is ‘scale up’. This makes the whole system latency sensitive. Regular expression matching is well studied, but state of the art hardware algorithms don’t reach the performance and memory targets needed for Pigasus. The reassembler: processing fast and slow.

Servers 128
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InnoDB Performance Optimization Basics

Percona

These guidelines work well for a wide range of applications, though the optimal settings, of course, depend on the workload. Hardware Memory The amount of RAM to be provisioned for database servers can vary greatly depending on the size of the database and the specific requirements of the company. I hope this helps!

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Snap: a microkernel approach to host networking

The Morning Paper

Here are the bombshell paragraphs: Our datacenter applications seek ever more CPU-efficient and lower-latency communication, which Pony Express delivers. The desire for CPU efficiency and lower latencies is easy to understand. Upgrades are also rolled out progressively across the cluster of course. Emphasis mine).

Network 92
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A case for managed and model-less inference serving

The Morning Paper

As we saw with the SOAP paper last time out, even with a fixed model variant and hardware there are a lot of different ways to map a training workload over the available hardware. First off there still is a model of course (but then there are servers hiding behind a serverless abstraction too!). autoscaling).