Pattern-Based Monitoring Knowledge Compiler for Server Platforms
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Solution Overview
Problem
Server monitoring tools face substantial development and maintenance costs due to the need for frequent updates and compatibility with various server versions and platforms, leading to inconsistent experiences for users.
Innovation Solution
A pattern-based high-level language is used to express server health and configuration monitoring knowledge, which is compiled into platform-specific rule packs, allowing for efficient and consistent monitoring across different platforms and versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If monitoring tools are updated frequently to maintain compatibility with various server versions and platforms, then monitoring effectiveness is improved, but development and maintenance costs increase substantially
Solution Approach 1:
The patent creates a universal monitoring knowledge representation that can be applied across multiple server versions and platforms. By defining monitoring knowledge in a platform-agnostic way that can be instantiated for different targets, the system achieves broad compatibility without requiring separate tooling for each platform, thereby reducing development and maintenance costs while maintaining monitoring effectiveness.
Solution Approach 2:
The patent segments monitoring knowledge into distinct, reusable components that can be independently managed and compiled. By breaking down monitoring logic into modular knowledge units that can be selectively applied to different server versions and platforms, the system reduces the complexity of maintaining monolithic monitoring tools while ensuring comprehensive coverage across multiple targets.
2Adaptability or versatility
If monitoring knowledge is represented in detailed platform-specific formats, then compatibility with specific tools is improved, but portability and efficiency decrease
Solution Approach 1:
The patent extracts the essential monitoring knowledge from platform-specific implementations and represents it in a simplified, platform-agnostic format. By separating the core monitoring logic from platform-specific details, the system achieves portability and efficiency while maintaining the ability to adapt to different tools through compilation to platform-specific rule packs when needed.
Solution Approach 2:
The patent introduces an intermediary representation layer between the high-level monitoring requirements and platform-specific implementations. This intermediate knowledge representation serves as a mediator that can be compiled to various target platforms, reducing the complexity of direct platform-specific programming while maintaining adaptability to different monitoring tools through the compilation process.
3Device complexity
If monitoring knowledge is represented in a concise and portable format, then maintenance costs are reduced, but direct usability by familiar tools decreases
Solution Approach 1:
The patent performs preliminary compilation of the concise, portable monitoring knowledge into platform-specific rule packs before deployment. By pre-compiling the high-level knowledge representations into tool-compatible formats, the system maintains the maintenance advantages of the concise format while ensuring direct usability by familiar monitoring tools through the pre-generated platform-specific outputs.
Solution Approach 2:
The patent uses an intermediary compilation process that translates the concise, portable knowledge representation into formats usable by familiar monitoring tools. This intermediary step preserves the maintenance benefits of the simplified source format while bridging the gap to tool-specific requirements, making the system both easy to maintain and directly usable by standard tools.
Data Source
AI summary
Monitoring knowledge is distilled into platform-nonspecific patterns of high-level language elements compiled into management packs or other rule packs targeting specific platforms. A server health and/or configuration monitoring knowledge compiler accepts distillation document(s) and target-specific information, and generates target-specific rule packs to be consumed by monitoring tools to monitor specific target platforms consistent with the platform-nonspecific monitoring elements. Computational rule pack generation is qualitatively different from manual rule pack creation. Plug-ins tailor the compiler to generate on-premises or cloud-based rule packs. Distillation element examples include monitor alert types, instructional content types, target attributes and tags, monitored item types, event alert types, performance collection types, policy monitor types, and threshold monitor types. Tags on monitoring elements indicate relevant products, locations, product features, monitoring tool features, contributors, etc. Runtime code is shared by multiple rule packs for different target platforms.