Training Task Queuing Cause Analysis via Clustering
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Solution Overview
Problem
In large-scale algorithm training, resource allocation and queuing issues lead to inefficiencies due to rapid exhaustion of CPU and GPU resources, causing tasks to wait in dispatching queues during user rush hours, impacting working efficiency.
Innovation Solution
A method and system that analyze the cause of training-task queuing by obtaining required and remaining resources, calculating distances between sample data and pre-generated cluster center data, and updating the cluster model iteratively to identify the cause of queuing, using clustering algorithms to categorize resource usage and load states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the platform dispatcher places tasks into a waiting queue when resources are insufficient, then resource allocation is maintained, but task processing speed and user waiting time deteriorate during rush hours
Solution Approach 1:
The system performs preliminary classification of queuing tasks by identifying their causes (resource insufficient, model too large, data quality issue, etc.) before full processing. This preliminary action allows the system to prepare appropriate handling strategies in advance, reducing the actual waiting time when resources become available.
Solution Approach 2:
The system provides feedback to users about the specific cause of their task queuing status. This feedback mechanism allows users to understand whether their task is waiting due to resource constraints, model size issues, or data problems, enabling them to adjust their submissions accordingly and improving overall system efficiency.
2Measurement precision
If the platform performs detailed resource checking and dispatching operations, then resource allocation accuracy is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system segments the task dispatching process into distinct stages: initial resource checking, queuing cause classification, and detailed dispatching operations. This segmentation allows each stage to focus on specific tasks, improving accuracy while managing complexity through modular processing.
Solution Approach 2:
The queuing cause classification acts as an intermediary layer between resource checking and detailed dispatching. This intermediary component simplifies the overall system by providing a structured intermediate state that bridges resource availability assessment and actual task allocation, making the system more manageable.
3Productivity
If the platform processes all tasks immediately when resources are available, then productivity is improved, but resource exhaustion and system instability occur during high-demand periods
Solution Approach 1:
The system dynamically adjusts its task processing strategy based on current resource availability and queuing conditions. By classifying queuing causes and adapting dispatching behavior accordingly, the system can process tasks at optimal rates without causing resource exhaustion, maintaining stability while maximizing productivity.
Solution Approach 2:
The system maintains continuous monitoring and classification of queuing tasks, ensuring that resource allocation decisions are always based on current system state. This continuous action allows the system to smoothly transition between different processing modes, preventing instability while maintaining high productivity when resources permit.
Data Source
AI summary
The present application discloses a method for analyzing a cause of training-task queuing, wherein the method includes the steps of: obtaining a required resource that is required by a training task inputted by a user and a remaining resource of a cluster; in responding to that the remaining resource does not satisfy the required resource, obtaining a plurality of cluster center data that are pre-generated in a cluster model; regarding the required resource and the remaining resource as sample data, and calculating distances between the sample data and each of the cluster center data; and feeding back a cause corresponding to the cluster center datum that has a minimum distance with the sample data. The present application further discloses a system, a computer device and a readable storage medium.