Probabilistic Pre-processing for Big Data Resource Allocation
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Solution Overview
Problem
In big data workflows, existing technologies fail to effectively manage resources during idle periods, leading to inefficiencies as they lack a method to proactively execute operations that are likely to be needed in the future, resulting in suboptimal use of resources and potential delays.
Innovation Solution
A probabilistic estimation method is employed to determine the value of pre-processing operations, allowing for proactive execution of operations by extracting feature values, identifying relevant operations, and allocating resources, even if they haven't been explicitly requested, based on probabilistic assessments of resource availability, operation completion times, and output features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are allocated to handle peak demand in big data workflows, then reliability is improved, but resources remain idle during low-demand periods, reducing productivity
Solution Approach 1:
The system performs preliminary actions by proactively executing operations before they are explicitly requested. The probabilistic estimation model predicts which operations are likely to be needed and executes them in advance during idle periods, transforming resource idle time into productive work that prepares data for future requests.
Solution Approach 2:
The system enables self-service through automated probabilistic assessment and decision-making. The probabilistic estimation model autonomously determines which operations to pre-process based on predicted demand patterns, eliminating the need for manual intervention to decide resource allocation during idle periods.
2Device complexity
If operations are executed only when explicitly requested, then device complexity is reduced, but wait time increases and productivity decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the probabilistic estimation model continuously learns from actual operation requests and execution patterns. This feedback refines the model's predictions over time, improving accuracy in identifying which operations should be pre-executed without requiring complex manual configuration.
3Productivity
If pre-processing operations are executed without probabilistic evaluation, then productivity improves through faster operation completion, but resource allocation becomes inefficient and reliability decreases
Solution Approach 1:
The system changes the parameter of operation execution timing from reactive (only when requested) to proactive (in advance based on predictions). The probabilistic estimation model evaluates multiple parameters including operation importance, predicted request timing, and resource availability to dynamically adjust when operations should be executed, optimizing both productivity and resource allocation reliability.
Data Source
AI summary
Methods and apparatus are provided for data operation pre-processing with a probabilistic estimation of operation value. An exemplary method comprises extracting feature values from a data set; identifying a set of operations that previously processed data sets comprising the extracted feature values; determining whether to execute an operation from the set of operations before an explicit request for the execution of the operation based on a probabilistic evaluation of a value of pre-processing the operation; and executing a set of instructions for the operation when it is determined that the operation is to be executed. The set of instructions comprises, e.g., stopping execution of other operations being executed, freeing resources required by the operation, and/or allocating resources required by the operation. An optional indirect component, optionally scaled by a moderating factor, estimates the value of pre-processing one or more additional operations enabled by features of data sets produced by the operation, and the resources to be allocated for the pre-processing of those operations.