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• Resource management in Kubernetes involves setting CPU and memory requests and limits for containers
• Memory limits result in termination of pods when exceeded, while CPU limits can lead to throttling or termination
• Setting appropriate requests and limits is crucial for efficient scheduling and avoiding noisy neighbors
• Developers should be aware of namespace limits and available resources when setting requests and limits
• In general, it is advisable to set memory requests equal to memory limits and avoid setting CPU limits
• Exceptions include cases where consistent workloads or overcommitment of memory are preferred

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• The network-aware framework uses a combination of plugins and algorithms to optimize workload scheduling based on network topology and bandwidth resources.
• The framework includes an application group and network topology controller, load watcher component, and a scheduler with filtering and scoring functions.
• The framework was tested with the Redis cluster application and was able to improve throughput by 20% on average.
• The framework is not yet production-ready but is expected to be included in the Seek scheduling community in the next few months.
• Future plans include adding a plugin for monitoring bandwidth and dynamically adjusting workload scheduling based on real-time network congestion.
• An anecdote was provided demonstrating the performance improvement of the online boutique application with the network-aware framework compared to the default Kubernetes scheduler.

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• Converged computing combines cloud and HPC technologies to enhance application performance, scalability, flexibility, and automation.
• Fully featured HPC scheduling in Kubernetes has not yet been achieved, and there are challenges in co-scheduling, throughput, job communication and coordination, portability, and resource heterogeneity.
• The Flux framework is an open-source project that solves the five key technical problems of converged computing.
• Cloud computing is becoming a dominant market force, and HPC needs to integrate research and development in software and hardware to avoid becoming isolated.
• LLNL is seeing demand for cloud technologies within HPC workflows, and there is potential to unite the two communities in a converged computing environment.

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• The elastic quota and job queue components are part of the Kubernetes scheduler and have been extensively tested.
• The components are compatible with various workload management systems and can be configured to meet specific needs.
• The goal is to make the components production-ready and widely adopted.
• The presentation mentions Alibaba and Apple as early adopters of the components.
• The components can be used for scheduling multiple jobs at the same time and ensuring that resources are not exceeded.
• The presentation also discusses the possibility of using the components for nomad-style scheduling and SLA-driven scheduling.