Affinity Rule Inference for Distributed Resource Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In conventional virtualized infrastructures, manually creating and managing affinity-type rules for hundreds or thousands of clients is cumbersome and requires significant manual operations, especially as conditions change over time.
Innovation Solution
A resource management system that automatically creates affinity-type rules using association inference information to determine resource associations, allowing for automated rule creation and modification during resource management operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually creating affinity-type rules for each client, then rule accuracy and control precision are improved, but management complexity and time consumption increase significantly
Solution Approach 1:
The system automatically infers affinity rules by analyzing client behavior patterns and resource usage data, allowing the system to self-manage rule creation without human intervention. The affinity rule inference module processes client requests, determines resource associations, and generates appropriate affinity rules automatically.
Solution Approach 2:
The system changes the approach from manual parameter specification to automated parameter inference. Instead of requiring administrators to manually define affinity rules with specific parameters, the system infers these parameters by analyzing actual client behavior and resource usage patterns.
2Adaptability or versatility
If manually modifying affinity-type rules in response to changing conditions, then rule adaptability is improved, but operational time and labor requirements increase
Solution Approach 1:
The system continuously monitors client behavior and resource usage patterns, using this feedback to dynamically infer and update affinity rules. When conditions change, the system analyzes new data and automatically adjusts affinity rules without requiring manual intervention.
Solution Approach 2:
The system proactively infers affinity rules in advance based on observed patterns, preparing adaptive rules before conditions actually change. This allows the system to respond quickly to changing conditions without requiring retrospective manual adjustments.
3Productivity
If increasing the number of clients in the system, then system capacity and functionality are improved, but the complexity of managing affinity rules increases
Solution Approach 1:
As the number of clients increases, the system's automatic affinity rule inference capability scales accordingly. The module processes a growing number of client requests and automatically generates appropriate rules for each client based on their individual behavior patterns and resource needs.
Solution Approach 2:
The system segments affinity rule management at the individual client level, where each client's affinity rules are independently inferred based on their specific behavior patterns. This segmentation allows the system to manage large numbers of clients efficiently without requiring centralized manual management.
Data Source
AI summary
A resource management system and method for automatically creating affinity-type rules for resource management in a distributed computer system uses association inference information for at least one resource to determine resource association between resources, which is used to automatically create an affinity-type rule for the resources. The affinity-type rule is considered when executing a resource management operation.