Program Object Caching with Reusable Preload State
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Solution Overview
Problem
Existing cache pool and preloading technologies for software programs face issues such as excessive or erroneous preloading, leading to performance degradation and high labor costs for maintaining preload policies in complex business scenarios.
Innovation Solution
A method and apparatus for caching program objects that automatically preload and save cache objects based on information collections, allowing reuse across program lifecycles and reducing the need for manual preload policy determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual preloading policy is used to determine which objects to preload, then the cache pool can be populated with relevant objects, but it requires significant labor cost and time for maintenance, especially in complex business scenarios
Solution Approach 1:
The system automatically determines preloading policies through monitoring and analysis of object access patterns, eliminating the need for manual intervention. The program autonomously identifies frequently accessed objects and updates cache strategies based on observed behavior, replacing manual maintenance with self-service automation
Solution Approach 2:
The system implements continuous monitoring of object access patterns and uses this feedback to dynamically adjust preloading policies. By analyzing access frequency and patterns, the system automatically refines cache strategies, creating a closed-loop system that improves cache accuracy without manual intervention
2Reliability
If excessive objects are preloaded into the cache pool, then the cache hit rate may increase, but it leads to performance degradation due to unnecessary computational overhead and memory consumption
Solution Approach 1:
Instead of preloading all possible objects or using fixed comprehensive rules, the system applies partial preloading based on monitored access patterns. It loads only the necessary subset of objects that are actually frequently accessed, avoiding the overhead of loading unnecessary objects while maintaining high cache hit rates
Solution Approach 2:
The preloading policy transitions from static manual configuration to dynamic automatic adjustment. The system continuously monitors access patterns and adapts the preloading strategy in real-time, loading objects based on current usage patterns rather than predetermined comprehensive rules, thus optimizing performance
3Adaptability or versatility
If manual preloading policy maintenance is required for each program start, then the preload strategy can be adjusted, but it consumes significant computational resources and human labor
Solution Approach 1:
The system automatically maintains and updates preloading policies by monitoring object access patterns during program execution. It eliminates manual intervention by autonomously identifying frequently accessed objects and adjusting cache strategies, achieving both adaptability and automation simultaneously
Solution Approach 2:
The monitoring and policy adjustment process runs continuously during program execution rather than requiring discrete manual updates at each program start. This continuous automatic adjustment maintains flexibility while eliminating repetitive manual labor and associated computational overhead
Data Source
AI summary
The disclosure provides a method, apparatus, device, and medium for caching a program object. The detailed description of this method includes: in response to a start of a target program, reading an information collection of a cache object from a storage device, preloading the cache object into a cache pool of the target program based on the information collection; and in response to a closure of the target program, saving the information collection of the cache object in the cache pool to the storage device. With this method, it is possible to eliminate the need to re-determine the preloaded object based on a manually maintained preloading policy each time the program starts, reduce computational consumption, and prevent the problem of degradation of program performance due to overloading or misloading of a cache object. In addition, the labor cost used to maintain the preload policy is reduced.