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FIFO vs. LIFO: Which Queueing Strategy Is Better for Availability and Latency?

DZone

As an engineer, you probably know that server performance under heavy load is crucial for maintaining the availability and responsiveness of your services. In this post, we'll explore both strategies through a simple simulation in Colab, allowing you to see the impact of changing parameters on system performance.

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Balancing Low Latency, High Availability, and Cloud Choice

VoltDB

Balancing Low Latency, High Availability and Cloud Choice Cloud hosting is no longer just an option — it’s now, in many cases, the default choice. Let’s look at the top cloud computing use cases, the use cases for which cloud probably isn’t the best route available, and the use cases where a hybrid approach may be best.

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Mastering Hybrid Cloud Strategy

Scalegrid

Mastering Hybrid Cloud Strategy Are you looking to leverage the best private and public cloud worlds to propel your business forward? A hybrid cloud strategy could be your answer. Understanding Hybrid Cloud Strategy A hybrid cloud merges the capabilities of public and private clouds into a singular, coherent system.

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Redis® Monitoring Strategies for 2024

Scalegrid

Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring. To monitor Redis® instances effectively, collect Redis metrics focusing on cache hit ratio, memory allocated, and latency threshold.

Strategy 130
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Plan Your Multi Cloud Strategy

Scalegrid

A well-planned multi cloud strategy can seriously upgrade your business’s tech game, making you more agile. Key Takeaways Multi-cloud strategies have become increasingly popular due to the need for flexibility, innovation, and the avoidance of vendor lock-in. They can also bolster uptime and limit latency issues or potential downtimes.

Strategy 130
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.

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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

In this post, I’m going to break these processes down into each of: ? What is the availability, configurability, and efficacy of each? Given that 66% of all websites (and 77% of all requests ) are running HTTP/2, I will not discuss concatenation strategies for HTTP/1.1 4,362ms of cumulative latency; 240ms of cumulative download.

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