Katalyst aims to provide a universal solution to help improve resource utilization and optimize the overall costs in the cloud. This is the core components in Katalyst system, including multiple agents and centralized components
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Support for recommending resource specifications for workloads #212
Kubernetes is widely adopted for its ability to manage containerized workloads efficiently. However, determining the appropriate resource specifications (CPU and memory) for workloads remains a challenge. Often, users over-provision resources to ensure performance stability, leading to wasted resources and increased costs. On the other hand, under-provisioning can result in performance degradation and service disruptions.
By introducing a resource recommendation feature, we can address these challenges and provide the following benefits:
Resource Efficiency: Users will be able to allocate resources more accurately, reducing waste and optimizing cost management.
Performance Optimization: Tailored recommendations will ensure that workloads have the resources they need to run optimally, minimizing both over-provisioning and performance bottlenecks.
Ease of Use: The automated recommendation process will simplify resource management for both experienced and novice Kubernetes users.
What would you like to be added?
This issue proposes the addition of a new feature that enhances Kubernetes resource utilization by providing the ability to recommend resource specifications for workloads. This feature would analyze historical usage patterns and real-time performance metrics of workloads to intelligently suggest optimal resource requests for CPU and memory.
ZhangsongLee
commented
1 year ago
Why is this needed?
Kubernetes is widely adopted for its ability to manage containerized workloads efficiently. However, determining the appropriate resource specifications (CPU and memory) for workloads remains a challenge. Often, users over-provision resources to ensure performance stability, leading to wasted resources and increased costs. On the other hand, under-provisioning can result in performance degradation and service disruptions. By introducing a resource recommendation feature, we can address these challenges and provide the following benefits:
What would you like to be added?
This issue proposes the addition of a new feature that enhances Kubernetes resource utilization by providing the ability to recommend resource specifications for workloads. This feature would analyze historical usage patterns and real-time performance metrics of workloads to intelligently suggest optimal resource requests for CPU and memory.