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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. This guide delves into how these systems work, the challenges they solve, and their essential role in businesses and technology.

Storage 130
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Effective management of memory stores with policies like LRU/LFU proactive monitoring of the replication process and advanced metrics such as cache hit ratio and persistence indicators are crucial for ensuring data integrity and optimizing Redis’s performance. offers the Software Watchdog specifically designed for this purpose.

Metrics 130
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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., Systems built with lots of microservices have different operational characteristics to those built from a small number of monoliths, we’d like to study and better understand those differences.

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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. This avoids thrashing caches too much for B and evens out the pressure on the L3 caches of the machine.

Cache 251
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Designing far memory data structures: think outside the box

The Morning Paper

Designing far memory data structures: think outside the box Aguilera et al., Therefore, if we want to make full use of one-sided far memory, we need to think carefully about the design of our data structures to make that access efficient. Processor caches can help to hide local accesses too, but not remote accesses.

Design 80
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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Compress objects, not cache lines: an object-based compressed memory hierarchy Tsai & Sanchez, ASPLOS’19. One of the important attributes of their design was easy and rapid deployment across an existing fleet. If we compress objects instead of cache lines though, we can get to a 56% compression ratio (c). Implications.

Cache 61
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AWS serverless services: Exploring your options

Dynatrace

This means you no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Instead of worrying about infrastructure management functions, such as capacity provisioning and hardware maintenance, teams can focus on application design, deployment, and delivery. Simplicity.