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
10 months 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.