Meta-Learning Resource Allocation for Heavy-Payload Tasks
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Solution Overview
Problem
Existing resource allocation methods in computing clusters lack adaptability and accuracy for managing diverse job payloads, leading to suboptimal resource utilization and prolonged job execution times due to inadequate utilization of historical data and intricate job behavior patterns.
Innovation Solution
A meta-learning diagnostic model is employed to dynamically adapt and evolve based on historical data, using a payload model and actuator engine to identify heavy payloads and dynamically reconfigure resources, incorporating unsupervised and supervised learning techniques to enhance predictive accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation methods are used, then system simplicity is maintained, but resource utilization efficiency deteriorates and job execution times increase
Solution Approach 1:
The patent implements dynamic resource allocation by training a machine learning model (payload model) that adapts to different job characteristics. The system transitions from static allocation to dynamic allocation based on real-time analysis of job payload features, CPU usage patterns, and throughput metrics, allowing resource allocation to evolve with changing workload conditions
Solution Approach 2:
The system employs self-service mechanisms through automated model training using historical task information and feedback loops. The payload model automatically identifies heavy payloads and predicts resource requirements without manual intervention, and the actuator engine autonomously adjusts resource allocation based on model predictions, reducing reliance on manual algorithm selection
2Adaptability or versatility
If existing resource allocation methods are used, then implementation simplicity is maintained, but adaptability to diverse job payloads deteriorates
Solution Approach 1:
The system changes allocation parameters dynamically by analyzing multiple job characteristics including payload size, CPU usage patterns, throughput metrics, and job type. The payload model adjusts resource allocation parameters based on these varying parameters, enabling adaptation to diverse job payloads through multi-dimensional parameter analysis rather than fixed allocation rules
Solution Approach 2:
The patent segments the resource allocation problem into distinct components: payload analysis module, model training module, prediction module, and actuator engine. This segmentation allows each component to specialize in specific aspects of adaptability, with the payload model handling prediction and the actuator engine handling execution, improving overall adaptability through modular architecture
3Measurement precision
If manual algorithm selection is used, then system complexity is reduced, but predictive accuracy deteriorates and execution times increase
Solution Approach 1:
The system implements feedback mechanisms where the payload model is continuously trained using historical task information and performance outcomes. The model receives feedback from actual job execution results, allowing it to refine its predictions of resource requirements and heavy payload identification, progressively improving predictive accuracy through iterative learning from real system behavior
Solution Approach 2:
The patent replaces manual algorithm selection with an automated machine learning-based payload model. Instead of manually choosing allocation algorithms, the system uses trained models that automatically predict resource requirements based on job characteristics, substituting human decision-making with data-driven automated predictions that achieve higher accuracy
4Productivity
If historical data is not utilized, then system complexity is reduced, but resource allocation accuracy deteriorates and execution times prolong
Solution Approach 1:
The system performs preliminary action by pre-training the payload model using historical task information before actual job execution. The model learns from past job patterns, CPU usage trends, and throughput characteristics in advance, enabling it to make accurate predictions about resource requirements for new jobs without processing delays during execution
Data Source
AI summary
Aspects of the disclosure related to dynamic task resource allocation. A computing platform may train a payload model using historical task information to determine whether a task contains a heavy payload. The computing platform may receive task information corresponding to a task. The computing platform may preprocess the task information. The computing platform may input the preprocessed task information into the trained payload model. The computing platform may extract a CPU percentage and throughput associated with the preprocessed task information. The computing platform may compare the CPU percentage to a CPU percentage threshold and the throughput to a throughput threshold. The computing platform may preempt a task corresponding to the task information based on both thresholds being exceeded.