Modular Diagnostic Engine Core for Runtime Fault Detection
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Solution Overview
Problem
Existing diagnostic systems for computing environments are costly, lack flexibility, consume significant resources, and are difficult to update, leading to inefficiencies in fault detection and remediation.
Innovation Solution
A lightweight diagnostic engine core with extensible modules that can be dynamically loaded and unloaded, allowing for flexible and efficient diagnostics through a configuration file that specifies which modules to use, along with a graph-based representation of module relationships for execution order and dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive diagnostic system is developed to address as many faults as possible, then diagnostic coverage is improved, but development time and cost increase significantly
Solution Approach 1:
The diagnostic system is segmented into a core engine and separate diagnostic modules. Each module addresses specific faults independently, allowing the system to achieve comprehensive coverage by combining multiple simple modules rather than developing one complex comprehensive system.
Solution Approach 2:
The diagnostic engine core serves as a universal platform that can execute multiple different diagnostic modules. This multi-functional core handles fault detection, execution management, and coordination across all modules, enabling comprehensive diagnostic coverage without requiring separate dedicated systems for each fault type.
2Reliability
If a comprehensive diagnostic system is developed to address as many faults as possible, then diagnostic coverage is improved, but personnel requirements and cost increase
Solution Approach 1:
By dividing the diagnostic system into independent modules, each can be developed, tested, and maintained separately by smaller teams. This segmentation reduces the overall personnel requirements compared to developing a monolithic comprehensive system, as each module can be handled by specialized small groups rather than requiring a large unified team.
Solution Approach 2:
The system allows dynamic configuration of which modules to load and execute based on the specific diagnostic needs. This parameter-based selection enables the system to adapt to different diagnostic requirements without requiring all possible modules to be fully developed and maintained, thereby reducing development cost for any given deployment scenario.
3Reliability
If the diagnostic system program code is made large to address more faults, then diagnostic coverage is improved, but the system becomes unwieldy and resource consumption increases
Solution Approach 1:
The diagnostic code base is segmented into separate modules that can be selectively loaded. This allows the system to maintain comprehensive coverage capability while keeping the active memory footprint small by only loading the modules relevant to current diagnostic needs, preventing the system from becoming unwieldy during execution.
Solution Approach 2:
The diagnostic system dynamically loads and unloads modules based on current requirements. This dynamic behavior allows the system to adapt its size and complexity at runtime, maintaining comprehensive coverage capability while minimizing actual resource consumption by only having active modules in memory during any given diagnostic operation.
4Reliability
If existing diagnostic systems are used, then fault detection capability is provided, but they consume significant processing and memory resources
Solution Approach 1:
By segmenting the diagnostic functionality into separate modules that are selectively loaded, the system reduces processing and memory resource consumption compared to running a comprehensive diagnostic system continuously. Only the necessary modules are loaded into memory and executed, minimizing resource usage while maintaining effective fault detection capability.
Solution Approach 2:
The dynamic loading and unloading of modules allows the system to adjust its resource consumption based on actual diagnostic needs. This dynamic behavior enables the system to maintain fault detection capability when needed while significantly reducing processing and memory resources during periods when comprehensive diagnostics are not required.
Data Source
AI summary
In some examples, a diagnostic engine core accesses configuration information specifying diagnostics to be performed on a collection of compute elements. Based on the configuration information, the diagnostic engine core selects diagnostic modules from a plurality of diagnostic modules, and generates a representation of a relationship of the selected diagnostic modules. The diagnostic engine core loads the selected diagnostic modules at runtime of the diagnostic engine core. After the loading, the diagnostic engine core triggers execution of the selected diagnostic modules in an execution order and according to to dependencies specified in the representation of the relationship, the selected diagnostic modules when executed performing the diagnostics on the collection of compute elements.