Stream Data Compression Prediction for Real-Time Batch Selection
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Solution Overview
Problem
Existing stream processing platforms like Dell SDP and Pravega lack data compression capabilities, and running a compression optimization engine for each stream batch is computationally costly, especially at the client side, where near real-time stream management requires high performance.
Innovation Solution
A predictive and adaptive data compression system that selects an optimal compressor based on historical data patterns and Service Level Agreement (SLA) constraints, using a training and inferencing architecture to predict the best compressor for a next portion of a data stream and embed it in a stream data platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a compression optimization engine is run for each stream batch, then compression performance can be optimized, but computational cost increases significantly
Solution Approach 1:
The system performs compression optimization in advance by training a machine learning model on historical stream batch data. The trained model is then deployed to predict optimal compression parameters for future batches, eliminating the need to run expensive optimization engines in real-time for each batch.
Solution Approach 2:
Instead of executing the full compression optimization engine for each stream batch, the system creates a simplified copy in the form of a trained machine learning model that captures the essential optimization logic. This model can be rapidly applied to multiple batches with minimal computational overhead.
2Productivity
If data compression is implemented in real-time stream processing, then bandwidth and storage efficiency improve, but system complexity increases
Solution Approach 1:
The system introduces a machine learning model as an intermediary between the raw stream data and the compression engine. This model predicts optimal compression parameters, simplifying the overall system architecture by replacing complex real-time optimization logic with a pre-trained prediction mechanism.
Solution Approach 2:
The system dynamically adjusts compression parameters based on predictions from the machine learning model, which analyzes patterns in the stream data. This allows the compression behavior to adapt to different data characteristics without requiring complex real-time decision-making logic.
3Manufacturing precision
If compression parameters are dynamically adjusted for each stream batch, then compression ratio improves, but processing time increases
Solution Approach 1:
The system pre-computes compression parameter adjustments during the model training phase using historical data. Once trained, the model can rapidly predict appropriate parameters for new batches without performing time-consuming optimization calculations in real-time.
Solution Approach 2:
The system replaces the mechanical process of running compression optimization algorithms for each batch with a machine learning-based prediction system. This substitution transforms a computationally intensive iterative process into a fast prediction operation.
Data Source
AI summary
One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.