Weight-Based Parameter Resampling for Dynamic Batch Size Tuning
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Solution Overview
Problem
Cloud computing systems face challenges in efficiently processing large volumes of data due to unpredictable network and computing resource availability, making it difficult to optimally tune batch sizes for external invocations, which can lead to inefficiencies and faults in data enrichment processes.
Innovation Solution
A method utilizing weight-based parameter resampling, where potential batch sizes are assigned weights based on their probability of selection, and adjusted based on feedback data from previous invocations, allowing the system to dynamically tune parameters for efficient data processing across iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of records are included per request to improve processing efficiency, then productivity is improved, but reliability deteriorates due to network conditions and computing resource availability variability
Solution Approach 1:
The patent implements dynamic batch size adjustment by monitoring feedback metrics (processing time, error rates, resource utilization) and adapting the number of records per request in real-time. This allows the system to optimize for productivity when conditions are favorable while maintaining reliability when resources are constrained, resolving the contradiction between processing efficiency and stability.
Solution Approach 2:
The system changes the parameter of batch size (number of records per request) based on observed system conditions and feedback. By adjusting this parameter dynamically rather than using a fixed value, the system can achieve high productivity when resources are abundant while maintaining reliability when conditions deteriorate, thus resolving the technical contradiction.
2Productivity
If batch size is increased to reduce the number of external invocations, then productivity is improved, but device complexity increases due to difficulty in dynamically predicting optimal parameters
Solution Approach 1:
The patent implements a feedback mechanism that monitors processing outcomes (success rates, latencies, resource usage) and uses this information to automatically adjust batch size parameters. This feedback loop eliminates the need for complex manual parameter prediction and tuning, allowing the system to achieve high throughput while keeping the control logic relatively simple and adaptive.
Solution Approach 2:
The system performs self-tuning of batch size parameters by automatically analyzing its own performance metrics and adjusting parameters accordingly. This self-service capability eliminates the need for external complex prediction mechanisms or manual intervention, achieving high productivity while maintaining manageable system complexity.
3Reliability
If batch size is decreased to improve adaptability to changing conditions, then reliability is improved, but productivity deteriorates due to increased number of invocations
Solution Approach 1:
The system dynamically adjusts batch size based on real-time conditions rather than using a fixed small size. When conditions are favorable (high resource availability, good network conditions), it increases batch size to maintain productivity. When conditions deteriorate, it decreases batch size to maintain reliability, thus resolving the contradiction between stability and throughput.
Solution Approach 2:
The patent changes the batch size parameter adaptively based on monitored system state and feedback metrics. This parameter adjustment allows the system to achieve reliability through smaller batches when needed while maintaining productivity through larger batches when conditions permit, eliminating the need to consistently use small batch sizes.
Data Source
AI summary
Techniques are disclosed for tuning external invocations utilizing weight-based parameter resampling. In one example, a computer system determines a plurality of samples, each sample being associated with a parameter value of a plurality of potential parameter values of a particular parameter. The computer system assigns weights to each of the parameter values, and then selects a first sample for processing via a first external invocation based on a weight of the parameter value of the first sample. The computer system then determines feedback data associated with a level of performance of the first external invocation. The computer system adjusts the weights of the parameter values of the particular parameter based on the feedback data. The computer system then selects a second sample of the plurality of samples to be processed via execution of a second external invocation based on the adjustment of weights of the parameter values.