Machine-Learning Cacheable Query Prediction for Lower Latency
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Solution Overview
Problem
Heterogeneous computing systems face challenges in efficiently retrieving data due to computationally expensive operations, leading to latency, error, traffic congestion, and reduced throughput, especially when handling large volumes of data and synchronous requests.
Innovation Solution
Implementing a system with trained machine learning models to predict cacheable queries based on current and previous requests, using identifiers and key-value pairs, and caching data associated with predicted requests to reduce latency and bandwidth consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is retrieved from remote computing systems in real-time, then data freshness is improved, but latency and bandwidth consumption increase
Solution Approach 1:
The system performs preliminary actions by predicting future data requests using machine learning models and pre-fetching the required data into cache stores before the actual requests arrive. This allows the system to have data ready in advance, reducing latency when clients make requests while maintaining data freshness through intelligent prediction rather than simple static caching
2Quantity of substance
If multiple synchronous requests are performed for data retrieval, then data completeness is improved, but traffic congestion and system errors increase
Solution Approach 1:
The system predicts which data will be requested and pre-fetches it into cache stores before the actual requests occur. This eliminates the need for multiple synchronous requests to remote systems, as the predicted data is already available locally, thereby maintaining data completeness while avoiding traffic congestion and system errors
Solution Approach 2:
The cache store acts as an intermediary between client devices and remote computing systems. By serving predicted requests from the cache, the system reduces direct traffic to remote systems, preventing congestion and errors while still providing complete data through the caching mechanism
3Loss of time
If predictive caching is implemented, then latency is reduced, but device complexity increases
Solution Approach 1:
The system employs machine learning models that automatically learn from request patterns and autonomously predict future data requests without requiring complex manual configuration. The models self-adjust and improve over time, reducing the operational complexity despite the advanced functionality, while significantly reducing latency through intelligent pre-fetching
Data Source
AI summary
Predicting cacheable queries is provided. For example, a system integrates one or more processors with a cache store to execute one or more microservices. The system receives a first request including an identifier and a set of key-value pairs. The system predicts a set of requests based on the identifier and the set of key-value pairs. The system identifies based on a comparison with a threshold metric, a subset of predicted requests from the set of requests indicative of one or more subsequent requests. The system constructs a set of labels. The system retrieves, via the set of labels and from a data repository remote from the client service, data for the subset of predicted requests. The system transmits a cache value from the cache store that corresponds to the subsequent request.