Workload Recommendation Engine for Data Cluster Hardware Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional schedulers face inefficiencies in allocating hardware resources for deep learning model training workloads due to inadequate information about resource requirements, leading to poor resource allocation and interruption of the training process.
Innovation Solution
A recommendation engine extracts workload features and uses machine learning models to predict performance and time on various hardware configurations, providing recommendations for optimal resource allocation, reducing user overhead and improving scheduling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ad hoc mechanisms are used to determine hardware platform selection, then implementation simplicity is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent introduces a recommendation engine as an intermediary component between the workload submission and hardware platform execution. This engine contains machine learning models that analyze workload characteristics and predict optimal hardware configurations, serving as a mediator that translates workload requirements into hardware selection decisions without requiring direct complex interactions between users and hardware platforms
Solution Approach 2:
The system performs preliminary analysis of workload characteristics and generates hardware configuration recommendations before the actual workload execution begins. The recommendation engine pre-processes workload information, predicts performance metrics, and identifies optimal hardware platforms in advance, allowing the scheduler to make informed decisions without real-time complexity
2Measurement precision
If more information about workload resource requirements is collected, then scheduling accuracy is improved, but information processing overhead increases
Solution Approach 1:
The recommendation engine extracts only the most relevant features from workload information, such as computational requirements, memory needs, and algorithm characteristics. Rather than processing all available workload data, the system identifies and extracts key features that are most predictive of hardware performance, reducing information processing overhead while maintaining scheduling accuracy
Solution Approach 2:
The system transforms raw workload information into standardized feature representations that are optimized for machine learning model processing. By changing the parameter representation from raw workload descriptions to structured feature vectors, the system enables efficient analysis with reduced computational overhead while improving the precision of hardware matching
3Loss of time
If manual hardware configuration selection is used, then user control is maintained, but time consumption increases
Solution Approach 1:
The system enables self-service by automatically analyzing workload characteristics and selecting optimal hardware configurations without requiring manual user intervention. The recommendation engine autonomously performs hardware platform selection based on workload requirements, significantly reducing time consumption while maintaining ease of operation through automated decision-making
Data Source
AI summary
Techniques described herein relate to systems, methods, and non-transitory computer readable mediums for performing workloads on hardware configurations. The method includes determining compliant hardware configurations of the data cluster using workload features associated with the workload, hardware specification information associated with hardware of data nodes of a data cluster, and a first machine learning model; generating performance predictions associated with the compliant hardware configurations using the workload features, a portion of the hardware specification information associated with the compliant hardware configurations, and a second machine learning model; and generating a recommendation using the performance predictions, and the recommendation specifies a hardware configuration of the compliant hardware configurations. The workload is performed using the recommendation.