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For your eyes only: improving Netflix video quality with neural networks

The Netflix TechBlog

Recently, we added another powerful tool to our arsenal: neural networks for video downscaling. In this tech blog, we describe how we improved Netflix video quality with neural networks, the challenges we faced and what lies ahead. How can neural networks fit into Netflix video encoding?

Network 292
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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. Firstly, managing virtual networks can be complex as networking in a virtual environment differs significantly from traditional networking. Challenges with running Hyper-V Working with Hyper-V can come with several challenges.

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Edge Computing Orchestration in IoT: Coordinating Distributed Workloads

DZone

This proximity to data generation reduces latency, conserves bandwidth and enables real-time decision-making. However, managing distributed workloads across various edge nodes in a scalable and efficient manner is a complex challenge.

IoT 195
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Understanding What Kubernetes Is Used For: The Key to Cloud-Native Efficiency

Percona

Kubernetes can be complex, which is why we offer comprehensive training that equips you and your team with the expertise and skills to manage database configurations, implement industry best practices, and carry out efficient backup and recovery procedures. Just consider the sheer number of people who stream Netflix every night!

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The Power of Caching: Boosting API Performance and Scalability

DZone

Benefits of Caching Improved performance: Caching eliminates the need to retrieve data from the original source every time, resulting in faster response times and reduced latency. Bandwidth optimization: Caching reduces the amount of data transferred over the network, minimizing bandwidth usage and improving efficiency.

Cache 246
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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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Snap: a microkernel approach to host networking

The Morning Paper

Snap: a microkernel approach to host networking Marty et al., This paper describes the networking stack, Snap , that has been running in production at Google for the last three years+. The desire for CPU efficiency and lower latencies is easy to understand. SOSP’19. Emphasis mine). It reminds me of ZeroMQ.

Network 92