Node Pre-configuration for Job Launch Time Reduction
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Solution Overview
Problem
Current computer systems face inefficiencies in job-launch time due to the need for manual configuration of computer nodes for each job, which can be time-consuming and resource-intensive, especially in superscalar and cloud-based systems.
Innovation Solution
A system and method that uses historic job data to predict and pre-configure computer nodes for future jobs using machine learning algorithms, such as neural networks, to reduce the time required for job launch by automatically setting up the necessary configurations before a job is submitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual configuration of computer nodes is performed for each job, then job launch time increases, but configuration accuracy is maintained
Solution Approach 1:
The system performs preliminary configuration of computer nodes by analyzing historical job data and predicting future configuration needs. Nodes are pre-configured with anticipated software, libraries, and settings before jobs are actually submitted, eliminating the time-consuming manual configuration step while maintaining accuracy through machine learning predictions.
Solution Approach 2:
The system enables self-service configuration by automatically analyzing job submission patterns and historical data to determine what configurations will be needed. The node configuration process becomes autonomous, with the system serving itself by predicting and implementing configurations without manual intervention.
2Productivity
If nodes are pre-configured based on predictions, then job-launch time is reduced, but prediction accuracy requirements increase
Solution Approach 1:
The system implements feedback mechanisms where actual job configurations and outcomes are fed back into the machine learning models. This continuous feedback loop allows the system to learn from prediction errors and improve accuracy over time, ensuring that pre-configuration predictions become increasingly precise while maintaining high job launch efficiency.
Solution Approach 2:
The configuration prediction system is dynamic and adaptive, continuously adjusting its predictions based on changing job patterns and historical data. Rather than using static rules, the system evolves its prediction accuracy through machine learning, allowing it to adapt to new job types and configuration requirements while maintaining reduced launch times.
3Ease of operation
If automatic pre-configuration is implemented, then manual effort is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning layer that sits between job submissions and node configuration. This intermediary automatically analyzes patterns and makes predictions, shielding users from the complexity of the prediction system while providing simple, automated configuration. The complexity is contained within the intermediary layer rather than being exposed to users.
4Reliability
If historical job data is analyzed for predictions, then configuration accuracy improves, but data processing time increases
Solution Approach 1:
The system performs preliminary analysis of historical job data during periods when nodes are not actively being configured for immediate jobs. By preprocessing and storing configuration patterns in advance, the system builds prediction models that can quickly determine future configurations without requiring extensive real-time data processing, thus maintaining high reliability while minimizing data processing time impact on job launches.
Data Source
AI summary
Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.