Plugin Execution Stability Management via Dynamic Process Isolation
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Solution Overview
Problem
Existing plugin execution modes either cause core application instability due to shared process execution or resource overconsumption in separate processes, making it difficult to locate and manage plugin issues, leading to degraded user experience and performance.
Innovation Solution
A system that dynamically determines plugin stability levels based on abnormal frequency and adjusts execution modes, using shared or independent loader processes to manage plugin execution, ensuring stable core application performance and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the plugin runs in the same process as the core application, then interaction efficiency is improved, but system stability deteriorates due to plugin bugs causing core application crashes
Solution Approach 1:
The system segments plugin execution into different process contexts based on stability levels. Stable plugins execute in the core application process for high efficiency, while unstable plugins execute in separate isolated processes to prevent system crashes. This segmentation resolves the contradiction by allowing high interaction efficiency for reliable plugins while isolating unstable ones to protect system stability.
Solution Approach 2:
The system dynamically adjusts the execution mode of plugins based on their measured stability levels. Plugins can transition between execution contexts (same process or separate process) as their stability characteristics change over time. This dynamic adaptation allows the system to optimize interaction efficiency while maintaining stability protection, resolving the fixed trade-off between speed and reliability.
2Reliability
If each plugin occupies a separate process, then system stability is improved, but resource consumption increases leading to degraded performance
Solution Approach 1:
The system applies different execution isolation levels to different plugins based on their individual stability characteristics. Rather than isolating all plugins in separate processes, only unstable plugins receive this treatment while stable plugins share the core application process. This local differentiation maintains system stability for problematic plugins while conserving resources for reliable ones, resolving the contradiction between stability and resource consumption.
3Use of energy by moving object
If all plugins run in a specific shared process, then resource consumption is reduced, but reliability deteriorates as one unstable plugin can affect all other plugins
Solution Approach 1:
The system segments plugins into different execution groups based on stability levels. Unstable plugins are isolated in their own separate processes while stable plugins can share the core application process. This segmentation prevents contamination between plugins - an unstable plugin cannot affect others - while still allowing resource-efficient sharing for stable plugins, resolving the contradiction between resource consumption and reliability.
4Device complexity
If plugins are executed without stability classification, then device complexity is reduced, but difficulty of detecting and measuring problems increases
Solution Approach 1:
The system introduces visual or categorical indicators (analogous to color changes) to mark plugins based on their stability levels. Stable plugins are marked differently from unstable ones, making it immediately apparent which plugins require special execution handling. This classification system simplifies execution management while simultaneously improving problem detection and localization, as issues can be quickly traced to plugins with known stability characteristics.
Data Source
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AI summary
Executing a plugin includes obtaining a stability level of the plugin to be executed, determining a plugin execution mode based at least in part on the stability level, and executing the plugin according to the plugin execution mode determined. The plugin execution mode is selected from a plurality of available plugin execution modes.