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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

These can help you ensure your system’s health and quickly perform root cause analysis of any performance-related issue you might be encountering. Evaluating factors like hit rate, which assesses cache efficiency level, or tracking key evictions from the cache are also essential elements during the Redis monitoring process.

Metrics 130
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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. They maintain fault tolerance and redundancy by replicating this information throughout various nodes in the system.

Storage 130
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Redis® Monitoring Strategies for 2024

Scalegrid

This includes latency, which is a major determinant in evaluating the reliability and performance of your Redis® instance, CPU usage to assess how much time it spends on tasks, operations such as reading/writing data from disk or network I/O, and memory utilization (also known as memory metrics).

Strategy 130
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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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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.

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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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How to Optimize Digital Experience and Operations with Dynatrace

Dynatrace

Reducing CPU Utilization to now only consume 15% of initially provisioned hardware. Missing Cache Settings – Make sure you cache resources that don’t change often on the browser or use a CDN. Too many fine-grained services leading to network and communication overhead. N+1 Query Pattern. Infrastructure Optimization.

Cache 195