How Netflix Shapes our Fleet for Efficiency and Reliability

The presentation "How Netflix Shapes our Fleet for Efficiency and Reliability" is delivered by Joseph Lynch and Argha C, who discuss strategies Netflix uses to optimize service efficiency and reliability globally.

Main Topics Discussed:

• Capacity Modeling: Netflix models capacity requirements automatically to optimize hardware use based on business criticality, workload characterization, and AWS hardware performance.
• Optimization Loop: Continuous monitoring and reshaping of fleets ensure that resources meet changing workload patterns cost-effectively while maintaining service availability.
• Global Deployment: Netflix utilizes a complex, multi-region infrastructure paired with a distributed CDN to manage diverse demand from various devices worldwide, such as mobiles, PCs, and TVs.
• Efficiency and Failures: Discussing the balance between resource usage and cost due to failures, highlighting that downtimes like Amazon's cost $20,000 per second.
• Workload Characteristics: Workloads are analyzed not only by utilization but also by their impact and recovery speed, aiming to minimize potential risks and losses.
• Hardware Variety: Understanding the variability and availability of different hardware generations enables optimized utilization.
• Load Shedding: Strategies to handle unpredictable shifts in demand by shedding non-critical loads to maintain service for high-priority tasks.

Key Insights:

• Efficiency is about matching hardware resources with workload needs while considering failure costs.
• The balance involves multiple considerations like traffic, capacity planning, and server type selection under varied conditions.
• Global infrastructure supports the need for seamless, reliable streaming across multiple devices.
• Adaptive management allows quick reactions to traffic changes, achieving significant recovery time reductions during spikes.

The session provides a comprehensive view of the technical strategies and operational frameworks that Netflix employs to maintain its global streaming service efficiently and reliably, adapting continuously to dynamic demand and capacity shifts.

This is the end of the AI-generated content.

Netflix runs on a complex multi-layer cloud architecture made up of thousands of services, caches, and databases. As hardware options, workload patterns, cost dynamics and the Netflix products evolve, the cost-optimal hardware and configuration for running our services is constantly changing. It is no longer sufficient in modern cloud computing to buy large amounts of the same shape of computer and try to pack every workload on that with large fixed buffers, both for efficiency and availability reasons. It is also no longer sufficient for platform teams to work 1:1 with every service team to optimize their hardware selection, this does not scale.

This talk shows an alternative strategy, where each workload is placed on price-optimal hardware using automated understanding of hardware performance combined with workload characterization. Furthermore, as workload patterns shift, we can continuously re-evaluate and react for every cluster to ensure business outcomes for minimal spend.

We will start with understanding how we automatically model capacity requirements, including key concepts like service buffer allocation based on business criticality. Then we will show how we marry this understanding of workload needs with a deep understanding of AWS hardware performance and pricing to place each workload on efficient hardware. Finally, we will walk through the continuously running optimization loop, which monitors, detects changes, and re-shapes our fleet to maintain business outcomes as load patterns constantly change.

Even with all this planning, our systems still face unexpected load shifts that exceed modeled bounds, so to close we will briefly cover how we manage traffic demand and compute supply to ensure we can maintain availability while intelligently and rapidly injecting capacity into the right server groups to keep Netflix up and running and our customers happily streaming.