Autonomic Computing Normalization Architecture
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Solution Overview
Problem
Autonomic computing systems face challenges in managing and communicating between heterogeneous computing elements due to disparate programming approaches and technologies, leading to robustness and scalability issues.
Innovation Solution
A technology-neutral architecture is implemented to enable efficient management of autonomic systems, allowing components with different programming approaches or technologies to communicate and exchange information seamlessly, using data and command normalization, semantic object data structures, and machine learning for configuration adjustments and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous computing elements use different programming approaches and technologies, then system diversity and functionality are improved, but communication and management between elements become difficult
Solution Approach 1:
The patent introduces a technology-neutral architecture as an intermediary layer between heterogeneous computing elements. This architecture includes common data structures, protocols, and interfaces that mediate communication between elements using different programming approaches, enabling them to interact seamlessly without direct compatibility issues
Solution Approach 2:
The patent implements a universal management framework that can handle multiple types of computing elements through a single technology-neutral platform. This framework provides multi-functional capabilities for monitoring, controlling, and coordinating diverse elements using standardized interfaces and data formats
2Adaptability or versatility
If additional software and hardware functionality is added for each combination of elements, then communication capability is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal technology-neutral architecture that provides a single set of communication protocols and data structures applicable to all computing element combinations. This eliminates the need for multiple specialized communication layers, reducing overall system complexity while maintaining broad compatibility
Solution Approach 2:
The patent segments the system into distinct layers: a technology-neutral management layer and element-specific implementation layers. This segmentation isolates communication complexity at the neutral layer while keeping element implementations simple and independent
3Ease of manufacture
If disparate programming models are used, then implementation flexibility is improved, but robustness and scalability are compromised
Solution Approach 1:
The patent standardizes key parameters such as data formats, communication protocols, and interface definitions across the technology-neutral architecture. This parameter standardization ensures consistent behavior and reliable interactions between elements from different programming models, enhancing system robustness and scalability
Data Source
AI summary
The disparate data and commands from are received from a managed resource (102) and have potentially different semantics. The disparate data and commands are processed according to rules received from an autonomic manager (112) to produce a single normalized view of this information. The actual state of the managed resource is determined from the normalized view of disparate data. The actual state of the managed resource (102) is compared to a desired state of the managed resource (102). When a match does not exist between the actual state and the desired state, a configuration adjustment to the managed resource (102) and/or another resource is determined to allow the actual state to be the same as the desired state. Then, the configuration adjustment is applied to the managed resource (102). When a match exists between the actual state and the desired state, maintenance functions associated with the managed resource (102) are performed.