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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience.

Traffic 339
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FIFO vs. LIFO: Which Queueing Strategy Is Better for Availability and Latency?

DZone

But what happens when traffic bursts overwhelm your system? 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. Queueing requests is a common solution, but what's the best approach: FIFO or LIFO?

Strategy 141
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. These essential data points heavily influence both stability and efficiency within the system.

Metrics 130
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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. This is where large-scale system migrations come into play.

Traffic 279
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What is cloud migration?

Dynatrace

A cloud migration strategy, however, provides technical optimization that’s also firmly rooted in the business value chain. Migrating to the cloud is a strategy many organizations pursue to streamline and consolidate their security efforts. Likewise, you can scale down when your application experiences decreased traffic.

Cloud 164
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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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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

which is difficult when troubleshooting distributed systems. If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls.