Autonomic Data Center Policy Adaptation via Capability Discovery
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Solution Overview
Problem
Existing data center management tools require manual updates by system administrators for each data center, limiting their ability to adapt to changing conditions and reducing efficiency, as they are configured for specific environments and lack self-adapting policy management.
Innovation Solution
A system with a discovery module that collects data to identify and catalog capabilities, dynamically selects policies from a directory that support these capabilities, and applies them for autonomic management, allowing for adaptive policy execution based on the data center's configuration and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual rule updates are performed by system administrators for each data center, then specific data center configurations can be managed, but the system lacks adaptability to changing conditions and requires continuous human intervention
Solution Approach 1:
The system enables policies to self-adapt to changing data center conditions through automated detection of capability changes and dynamic policy selection, eliminating the need for manual administrator intervention. The autonomic manager automatically monitors capability catalogs and adjusts policy applications based on detected changes.
Solution Approach 2:
The policy management system transitions from static, manually-configured rules to dynamic, automatically adapting policies. The system continuously monitors data center capabilities and dynamically selects appropriate policies from the policy directory based on current conditions, allowing policies to evolve with the environment.
2Adaptability or versatility
If fixed rules are configured for specific data centers, then initial management functionality is provided, but the system cannot automatically adapt to different data center configurations or changes
Solution Approach 1:
The system implements continuous feedback loops where the autonomic manager monitors data center capabilities, detects changes in the capability catalog, and uses this information to automatically select and adjust policies. This closed-loop feedback enables automatic adaptation without human intervention.
Solution Approach 2:
The system pre-configures a policy directory containing multiple policies with defined capabilities and requirements before deployment. When the data center environment changes, the system can quickly select from these pre-prepared policies rather than creating new ones, enabling rapid automatic adaptation.
3Productivity
If system administrators manually manage each data center environment, then specific customization is possible, but productivity and efficiency are reduced due to repetitive administrative tasks
Solution Approach 1:
The system creates a universal autonomic management framework that can handle multiple data center environments with different configurations through a single policy directory and capability-based selection mechanism. This multi-functional approach eliminates the need for separate manual management of each data center while maintaining customization through capability-specific policy selection.
Data Source
AI summary
A method, system, and article for autonomizing autonomic management of a data center, with the data center having at least one computer system and an associated component. Data is collected from the data center and used as input to identify a data center policy. A set of capabilities of elements of the data center are detected and cataloged based upon the collected data. At least one policy is dynamically selected from at least one set of master policies in a policy directory with the selected policy to support the cataloged capabilities of the data center, and to dynamically control selective application and to adapt parameters for quality of service. The selected policy is applied to manage the data center.