Runtime Estimation for Machine Learning Data Processing Pipelines
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Solution Overview
Problem
Existing technologies face challenges in accurately estimating the runtime of data processing pipelines implementing machine learning models, particularly due to variations in timing definitions across different cloud computing platforms.
Innovation Solution
A system and method for estimating the runtime of a data processing pipeline that includes receiving inputs for constructing the pipeline, determining a first runtime that includes non-computation and computation times, and executing the pipeline based on the runtime not exceeding an available time budget. The computation time is computed using a computation time model trained on timing information from test and user trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If runtime estimation is performed using existing technologies, then the pipeline execution can be scheduled, but the estimation accuracy is poor due to variations in timing definitions across different cloud computing platforms
Solution Approach 1:
The patent introduces a standardized timing measurement intermediary layer that mediates between the diverse cloud computing platforms and the runtime estimation system. This intermediary defines consistent timing checkpoints and measurement protocols that work uniformly across different platforms, eliminating the variability caused by platform-specific timing definitions while maintaining broad platform compatibility
Solution Approach 2:
The patent changes the measurement parameters by defining specific, standardized timing checkpoints (e.g., pipeline start time, individual operation start/end times, completion time) that can be consistently measured across different platforms. By transforming the measurement approach from platform-dependent to parameter-standardized, the system achieves both high estimation accuracy and cross-platform adaptability
2Reliability
If multiple machine learning trials are executed with different parameters, then the model performance is optimized, but the total computation time increases
Solution Approach 1:
The patent applies preliminary action by performing runtime estimation and resource requirement analysis before executing the machine learning trials. This allows the system to plan the trial execution sequence, allocate resources efficiently, and identify potential bottlenecks in advance, thereby optimizing the overall computation time while maintaining model performance through comprehensive parameter exploration
Solution Approach 2:
The patent implements partial action by allowing users to specify a maximum number of trials or time budget, and the system executes trials partially based on available resources and estimated runtime. This approach balances the need for thorough model optimization with practical constraints on computation time, executing enough trials to achieve satisfactory performance without unnecessarily extending the total duration
3Productivity
If the data processing pipeline is executed without accurate runtime estimation, then the pipeline can run freely, but the time budget allocation is inefficient
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring actual runtime against estimated runtime during pipeline execution. This feedback allows the system to adjust resource allocation dynamically, provide accurate completion time predictions to users, and improve future runtime estimations based on observed deviations, thereby enhancing time budget allocation efficiency and preventing excessive execution time
Data Source
AI summary
Inputs may be received for constructing a data processing pipeline configured to implement an process to generate a machine learning model for performing a task associated with an input dataset. The process may include a plurality of machine learning trials, each of which applying, to a training dataset and/or a validation dataset generated based on the input dataset, a different type of machine learning model and/or a different set of trial parameters. The machine learning model being generated based on a result of the plurality of machine learning trials. A runtime estimate for the process to generate the machine learning model may be determined. The runtime estimate may enable the allocation of a sufficient time budget for the process. Moreover, the process may be executed if the runtime of the process does not exceed the available time budget.