Cache Warm Up Based on User Request Prediction
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Solution Overview
Problem
Existing cache warm up processes in data processing systems are inefficient, often requiring extensive computation and large cache spaces, and may miss replicating hotspot data, while simulated traffic can increase system load and cause bottlenecks.
Innovation Solution
A system that predicts cache replication data based on user requests, generating a control signal for the replication proportion to determine the volume of data to be replicated, allowing for efficient cache warm up by prioritizing hotspot data and avoiding increased system load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system traverses the database to find all data and replicate it into the cache in advance, then the cache can be warmed up completely, but it requires extensive computation and large cache space
Solution Approach 1:
The patent applies preliminary action by proactively replicating data into the cache before actual user requests occur. The system uses prediction models to anticipate which data will be requested and replicates that data in advance, rather than waiting for requests and reacting then. This is achieved through cache warm-up mechanisms that use historical data and machine learning predictions to pre-load relevant data into the cache memory.
Solution Approach 2:
The patent applies local quality by selectively replicating only the most relevant and frequently requested data into the cache, rather than replicating all database data uniformly. The system identifies and prioritizes 'hotspot data' - the local subset of data with highest request probability - and focuses replication resources on that specific portion, optimizing both computation efficiency and cache utilization.
2Productivity
If the system stores only a subset of data in the cache to improve efficiency, then computation and storage resources are reduced, but the most demanded hotspot data may not be in the subset
Solution Approach 1:
The patent applies feedback mechanisms by continuously monitoring actual user request patterns and using this information to refine prediction models. The system learns from real access behavior data, adjusts its predictions about which data will be requested, and adapts the cache warm-up strategy accordingly. This feedback loop ensures that the subset of data replicated into the cache increasingly aligns with actual demand patterns.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the replication proportion and cache warm-up strategy based on system conditions. The system can modify parameters such as the proportion of data to replicate, the selection criteria for hotspot data, and the timing of replication operations. This allows the system to adapt to changing data access patterns and optimize the balance between efficiency and hotspot data availability.
3Measurement precision
If the system uses simulated traffic to identify the most demanded contents for cache warm up, then data can be selected for replication, but the simulated traffic increases the load on the system and may cause bottleneck and unavailability
Solution Approach 1:
The patent applies copying by creating virtual representations or models of actual user request patterns rather than executing physical simulated traffic. The system uses prediction models that replicate the statistical characteristics of real demand patterns without actually generating artificial traffic flows. This allows the system to analyze and predict hotspot data based on copied patterns from historical data, avoiding the harmful effects of actual simulated traffic execution.
Data Source
AI summary
One embodiment of the present invention provides a system for efficiently warming up a cache. During operation, the system determines a cache prediction based on a user request. The cache prediction determines replication data from a database; the replication data is data that is to be replicated in the cache from the database for warming up the cache. The system generates a control signal comprising a replication proportion, which determines the volume of the replication data, for the replication model. The system then stores the replication data prior to receiving a request for the replication data, thereby warming up the cache with the replication data.