Cluster Configuration Environment for Workload Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed computing clusters face inefficiencies due to the complexity of configuring over 600 parameters, leading to performance issues like memory starvation, especially for novice users who struggle to optimize cluster resources for various workloads.
Innovation Solution
A cluster configuration environment with a manager, workload manager, optimization formula manager, and databases to evaluate workloads and determine optimal hardware configurations, using service optimization formulas to set configuration parameters efficiently, reducing the need for manual tuning and preventing memory starvation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration of cluster parameters is used, then flexibility and adaptability are maintained, but configuration complexity and time consumption increase significantly
Solution Approach 1:
The system automatically configures cluster parameters by analyzing workload characteristics and selecting optimal settings from predefined configurations. The workload analyzer and configuration selector components enable the system to self-configure without requiring manual intervention, thereby increasing configuration speed while managing complexity through automated decision-making
Solution Approach 2:
The system changes configuration parameters dynamically based on workload analysis. By analyzing workload characteristics and transitioning between different predefined configurations, the system optimizes cluster performance automatically, resolving the contradiction between configuration speed and complexity management
2Manufacturing precision
If detailed manual tuning of cluster parameters is performed, then optimization precision is improved, but time consumption and operational difficulty increase
Solution Approach 1:
The system performs preliminary analysis of workload characteristics before configuration. By pre-analyzing workload patterns and pre-selecting optimal configurations, the system achieves high optimization precision without requiring time-consuming manual tuning during execution
Solution Approach 2:
The system copies and adapts predefined optimal configurations based on analyzed workload characteristics. Instead of creating configurations from scratch through manual tuning, the system selects and adapts proven configurations, achieving precision while significantly reducing time investment
3Productivity
If cluster resources are allocated without optimization formulas, then ease of operation is maintained, but resource efficiency and performance deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where workload analysis continuously informs configuration selections. By monitoring workload characteristics and adjusting configurations accordingly, the system improves resource efficiency while maintaining operational simplicity through automated feedback-driven optimization
Solution Approach 2:
The system introduces intermediary components (workload analyzer, configuration selector) that mediate between user workload definitions and cluster configuration. These intermediaries translate high-level workload requirements into optimized configurations, improving efficiency while preserving ease of operation for users
4Reliability
If novice users configure cluster parameters without guidance, then operational flexibility is maintained, but configuration accuracy and performance optimization deteriorate
Solution Approach 1:
The system provides self-service configuration guidance where the workload analyzer automatically identifies appropriate configurations based on analyzed workload characteristics. This eliminates the need for novice users to manually navigate complex parameters while maintaining configuration reliability through automated, data-driven decisions
Solution Approach 2:
The system automatically changes configuration parameters based on workload analysis results. By dynamically selecting parameters and their optimal values according to analyzed workload characteristics, the system ensures reliable configurations for novice users while managing complexity through automated parameter transformation
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to improve cluster efficiency. An example apparatus includes a cluster manager to identify cluster resource details to execute a workload, a workload manager to parse the workload to identify services to be executed by cluster resources, and an optimization formula manager to identify service optimization formulas associated with respective ones of the identified services, and improve cluster resource efficiency by generating a cluster formula configuration to calculate cluster parameter values for the cluster resources.