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• Networking is the most stressing for the control plane and where the biggest number of issues are seen
• Understanding the size of churn forward or observed services is a significant factor in workload division
• The current scalability limit of 5000 nodes is not a hard limit and there are no plans to push it further in open source
• External factors like third-party controllers and ecosystem improvements need to be addressed
• Using the watch protocol for getting large collections of data can help with memory consumption and system throughput
• Graceful shutdowns can prevent the control plane from being blown out by hundreds of thousands of watches
• Optimizations should be balanced with complexity versus return on investment trade-off

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• Using immutable secrets can make Kubernetes API more reliable and reduce pressure on API servers
• Priority and fairness are heavily worked on to increase Kubernetes reliability
• Cluster loader two is a tool used for scalability testing in Kubernetes
• Cubemark is a simulation of the cluster used for scalability testing instead of running 5000 nodes
• Whole nodes and hollow nodes are used in Cubemark to simulate regular nodes without actually running pods
• Hollow cube proxy is a part of Kubernetes that puts pressure on the API server

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• SIG Scalability is focused on defining what scalability means for Kubernetes and executing towards those goals
• They work with individual SIGs to ensure improvements are made and contribute to cross-SIG improvements
• Monitoring and measuring current scalability levels is critical to understanding progress towards goals
• Guarding against performance regressions is important to maintain scalability
• Scalability is a job for everyone in the community, not just a small group