Remove Efficiency Remove Infrastructure Remove Latency
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. 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.

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently.

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Optimize your environment: Unveiling Dynatrace Hyper-V extension for enhanced performance and efficient troubleshooting

Dynatrace

This leads to a more efficient and streamlined experience for users. Lastly, monitoring and maintaining system health within a virtual environment, which includes efficient troubleshooting and issue resolution, can pose a significant challenge for IT teams.

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Dynatrace on Microsoft Azure in Australia enables regional customers to leverage AI-powered observability

Dynatrace

As modern multicloud environments become more distributed and complex, having real-time insights into applications and infrastructure while keeping data residency in local markets is crucial. This local SaaS presence minimizes latency and maximizes the speed and reliability of data access. The result?

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Investigation of a Workbench UI Latency Issue

The Netflix TechBlog

Using this approach, we observed latencies ranging from 1 to 10 seconds, averaging 7.4 To efficiently utilize our compute resources, Titus employs a CPU oversubscription feature , meaning the combined virtual CPUs allocated to containers exceed the number of available physical CPUs on a Titus agent. We then exported the .har

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Vidhya Arvind , Rajasekhar Ummadisetty , Joey Lynch , Vinay Chella Introduction At Netflix our ability to deliver seamless, high-quality, streaming experiences to millions of users hinges on robust, global backend infrastructure. This model supports both simple and complex data models, balancing flexibility and efficiency.

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Noisy Neighbor Detection with eBPF

The Netflix TechBlog

The first step is determining whether the problem originates from the application or the underlying infrastructure. Learn how Linux kernel instrumentation can improve your infrastructure observability with deeper insights and enhanced monitoring. We then calculate the run queue latency by simply subtracting the timestamps.

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