Container Orchestration Benchmarking via Configurable Resource Managers
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Solution Overview
Problem
There is a lack of standard, accurate, and easy-to-use benchmarking tools for evaluating the performance and scalability of container orchestration platforms like Kubernetes, which hinders understanding of the implications of increasing workloads on complex physical and virtual system stacks.
Innovation Solution
A system and method for benchmarking container orchestration platforms using configuration files to create resource managers for specified resource types, executing benchmark actions, and collecting performance metrics, which can be displayed for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing benchmarking tools are used for container orchestration platforms, then basic performance measurement is possible, but accuracy and precision of performance metrics are insufficient
Solution Approach 1:
The benchmarking system is segmented into distinct resource managers for different container orchestration resource types (Pods, Services, Deployments, etc.). Each resource manager independently benchmarks specific resource types, allowing precise measurement of performance metrics for each resource category while maintaining modular system complexity.
Solution Approach 2:
The benchmarking framework implements a universal resource manager architecture that can handle multiple container orchestration resource types through a common interface. This multi-functional design enables accurate benchmarking across diverse resource types without proportionally increasing system complexity, as the same core benchmarking engine serves multiple purposes.
2Reliability
If comprehensive benchmarking of all container orchestration resource types is performed, then complete performance evaluation is achieved, but ease of operation decreases
Solution Approach 1:
The benchmarking system dynamically adapts to user needs by allowing configuration of specific resource types and benchmark operations through configurable parameters. Users can selectively enable or disable benchmarking for different resource types (Pods, Services, Deployments, etc.), making the tool easy to operate for specific scenarios while maintaining the capability for comprehensive evaluation when needed.
Solution Approach 2:
The framework provides self-service capabilities through automated resource manager creation and configuration based on user specifications. The system automatically instantiates appropriate resource managers and executes benchmarks without requiring manual intervention, simplifying operation while ensuring complete performance evaluation across selected resource types.
3Measurement precision
If detailed performance metrics are collected for all benchmark actions, then measurement precision is improved, but loss of time in data collection and processing increases
Solution Approach 1:
The system extracts and collects only the specific performance metrics that are relevant to each benchmark operation and resource type. Rather than universally collecting all possible metrics, the framework selectively gathers detailed measurements for each benchmark action, reducing data collection time while maintaining measurement precision for the most important performance indicators.
Solution Approach 2:
Different levels of measurement detail are applied locally to different resource types and benchmark operations based on their specific requirements. The framework collects detailed performance metrics where needed (for critical resource types or operations) and uses streamlined collection for less critical benchmarks, optimizing the balance between measurement precision and data collection time.
Data Source
AI summary
System and method for benchmarking a container orchestration platform in a computing environment uses data contained in a configuration file, which specifies at least one benchmark operation having benchmark actions for container orchestration resource types to be performed in the container orchestration platform, to create a resource manager for each container orchestration resource type specified in the configuration file. Using the created resource managers, a routine for each benchmark action is spawned and executed on container orchestration objects in the container orchestration platform. As the routines are executed, performance metrics are collected and at least some of the collected performance metrics are displayed.