Document Ingestion Time Prediction via ML Resource Allocation
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Solution Overview
Problem
Current question answering systems face challenges in predicting document ingestion time, leading to inefficient load balancing and delayed completion due to the inability to accurately assess how long each document will take to ingest, often resulting in overloading of processing-intensive documents on the same computing node.
Innovation Solution
A mechanism that annotates document characteristics, builds a machine learning model to predict ingestion time based on these features, and intelligently assigns documents to resources for optimized processing, excluding or delaying certain features as needed to enhance ingestion performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If documents are assigned to data processing resources without predicting ingestion time, then resource allocation is simple and quick, but load balancing is poor and processing efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of document characteristics and predicts ingestion time before assigning documents to processing resources. This advance prediction enables better load balancing and resource allocation decisions, resolving the contradiction by adding a prediction step that improves efficiency without significantly increasing overall system complexity
Solution Approach 2:
The system uses historical ingestion data and document features to create predictive models that continuously improve resource allocation. The feedback loop from actual ingestion performance refines future predictions, enabling efficient resource allocation while maintaining manageable system complexity through iterative optimization
2Speed
If processing-intensive documents are assigned to the same computing node for ease of management, then resource coordination is simplified, but processing speed decreases due to node overload
Solution Approach 1:
The system applies different resource assignment strategies based on document characteristics and local node conditions. Processing-intensive documents are routed to nodes with available capacity, while lighter documents can be handled by any node. This localized optimization maintains operational simplicity while significantly improving processing speed through intelligent distribution
3Loss of time
If the system waits for complete document analysis before assignment, then assignment accuracy is improved, but ingestion time increases
Solution Approach 1:
The system performs partial analysis of document characteristics immediately upon receipt to generate preliminary ingestion time estimates, enabling rapid initial assignment. More complete analysis occurs in the background during processing. This partial action approach significantly reduces ingestion time while maintaining sufficient prediction accuracy for effective load balancing
Data Source
AI summary
A mechanism is provided in a data processing system for predicting and enhancing ingestion time for a set of input documents. The mechanism receives a set of documents to be added to a corpus of the data processing system. The mechanism records document features of each document within the set of documents using an annotation engine within the data processing system. The mechanism predicts an ingestion time for each document within the set of documents based on the document characteristics and a machine learning model. The mechanism assigns the set of documents to data processing system resources to be processed based on the predicted ingestion time for each document.