Intelligent Adapter Pool for Cloud Service Management
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Solution Overview
Problem
Current cloud service management approaches require cloud platform administrators to install, configure, and maintain separate tools for various types of service management information, making it difficult to integrate these tools effectively and adapt to new data sources for evolving service requirements.
Innovation Solution
A computer-implemented method that maintains an adapter pool for collecting and parsing service management information, automatically creating and selecting new adapters based on detected data sources, allowing for intelligent integration and adaptation of new data sources within the cloud platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate tools are used to obtain each piece of service management information, then comprehensive information coverage is achieved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent merges multiple separate monitoring tools into a unified monitoring system that uses a single agent deployed on each managed system. This agent collects diverse service management information (power supply, cooling, hardware configuration, hypervisor, virtual machines, operating systems, middleware, applications, and user data) through standardized interfaces, eliminating the need to integrate multiple separate tools while maintaining comprehensive information coverage.
Solution Approach 2:
The monitoring agent is designed as a universal component that can collect multiple types of service management information from a single deployment. The agent implements standardized interfaces that allow it to gather diverse data types (hardware, software, virtualization, user data) through a single unified tool, providing multi-functionality without requiring separate specialized tools for each information type.
2Adaptability or versatility
If traditional methods are used to adapt new data sources, then service requirements can be fulfilled, but adaptation difficulty and time increase
Solution Approach 1:
The system implements self-service adaptation through automated agent deployment and configuration. When new data sources or service requirements are introduced, the system automatically deploys appropriate monitoring agents with standardized interfaces, eliminating the need for manual configuration and reducing adaptation time. The agents self-configure to collect required information from diverse data sources including cloud infrastructure, virtualization layers, and application environments.
Solution Approach 2:
The monitoring functionality is segmented into independent, standardized agent components that can be individually deployed and configured for different data sources. Each agent is a self-contained unit that implements standardized interfaces, allowing new data sources to be adapted by simply deploying additional agent instances rather than reconfiguring the entire monitoring system, thereby reducing adaptation time and complexity.
Data Source
AI summary
Intelligent information adapter generation for service management. Managing selection of adapters from and adapter pool to use for collecting the service management information includes, based on adding a data source, selecting an adapter to use for collecting service management information from the added data source, the selecting including automatically creating and selecting a new adapter, the new adapter being created based on access information to access the added data source, a resource type of the portion of service management information collected from the added data source, and a desired performance indicator that the new adapter is to parse out from the portion of service management information that the new adapter collects.