Prediction-Based Token Allocation for Cloud Job Runtimes
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Solution Overview
Problem
Current cloud data services face inefficiencies in resource allocation for computational tasks due to the lack of tools to understand the relationship between allocated resources and execution time, leading to either wasteful over-allocation or prolonged runtimes if optimal resource levels are not specified.
Innovation Solution
A prediction-based token allocation system using machine learning models, trained with historical data including job characteristics and runtime results, to estimate the required token count for user-submitted jobs, optimizing resource allocation and reducing wastage or prolonged runtimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed number or fixed percentage of tokens is allocated by default, then resource allocation is simple and automatic, but resource efficiency deteriorates due to wasteful over-allocation or insufficient allocation
Solution Approach 1:
The system automatically generates token allocation recommendations using machine learning models trained on historical job data. The token estimator service autonomously analyzes job characteristics and predicts optimal token counts without requiring user intervention, thereby maintaining ease of operation while improving resource efficiency through data-driven decisions
Solution Approach 2:
The system dynamically adjusts token allocation parameters based on learned patterns from historical data. By changing from static default values to dynamic, prediction-based token counts, the system optimizes resource efficiency while maintaining simple operation through automated parameter adjustment
2Productivity
If users manually specify optimal token counts, then resource efficiency improves, but operation complexity increases and ease of use deteriorates
Solution Approach 1:
The token estimator service performs self-service by automatically generating token allocation recommendations based on historical job data and machine learning models. Users simply submit their jobs without needing to manually calculate or specify token counts, maintaining operational simplicity while achieving optimized resource allocation through the system's autonomous analysis
Solution Approach 2:
The system performs preliminary analysis of job characteristics using trained machine learning models before job execution. By pre-calculating optimal token allocations based on historical patterns and job metadata, the system eliminates the need for users to perform complex resource planning, thereby maintaining ease of use while improving resource efficiency
3Loss of energy
If insufficient tokens are allocated, then resource wastage is reduced, but job runtime increases
Solution Approach 1:
The system optimizes the token allocation parameter by predicting the precise number of tokens needed for each job based on historical data and job characteristics. This dynamic parameter adjustment ensures sufficient tokens are allocated to prevent runtime extensions while avoiding excessive allocation that would cause resource wastage, thereby balancing both concerns through data-driven optimization
Solution Approach 2:
The system uses feedback from historical job execution data to continuously improve token allocation predictions. By analyzing past job performance, runtime, and token consumption patterns, the machine learning models learn to predict optimal token counts that prevent both resource wastage and runtime extensions, creating a feedback loop that optimizes the balance between these competing objectives
4Duration of action of moving object
If excessive tokens are allocated, then job completion time is reduced, but resource wastage increases
Solution Approach 1:
The system dynamically adjusts the token allocation parameter from static defaults to optimized values based on historical job data and predicted job characteristics. This parameter optimization ensures tokens are allocated precisely at the level needed for efficient job completion, preventing both excessive allocation (resource wastage) and insufficient allocation (extended runtime), thereby resolving the contradiction through data-driven parameter tuning
Solution Approach 2:
The system replaces manual or heuristic-based token allocation mechanisms with machine learning-based prediction. By substituting the mechanical/manual process of token specification with an automated ML model that analyzes historical patterns, the system achieves precise token allocation that optimizes the balance between job completion time and resource wastage without requiring user expertise or trial-and-error approaches
Data Source
AI summary
Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.