Adaptable Software Resource Managers for Atypical Usage Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional product and software design methodologies often result in sub-optimal performance when products are used in atypical manners, as they are optimized for 'typical' usage patterns, leading to inefficient resource management in computer-executable systems.
Innovation Solution
An adaptable resource manager that can utilize multiple algorithms and mechanisms to dynamically adjust resource allocation based on inferred or programmatically indicated user intentions, supporting various modes of operation and environmental constraints to optimize resource usage across different scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource managers are designed to optimize for typical usage patterns, then performance for typical usage is improved, but performance for atypical usage deteriorates
Solution Approach 1:
The resource manager dynamically switches between multiple resource management algorithms based on detected usage patterns. Instead of being static and optimized for a single typical usage scenario, the system adapts its behavior in real-time by selecting from multiple algorithms (e.g., round-robin, priority-based, fair-share) depending on whether the current usage matches typical patterns or represents atypical usage scenarios.
Solution Approach 2:
The system changes the parameters of resource management by maintaining multiple algorithms with different optimization characteristics and selecting the appropriate algorithm based on usage pattern detection. This allows the resource manager to adjust its operational parameters (which algorithm is active) based on whether typical or atypical usage is detected, thereby resolving the contradiction between optimizing for typical usage while maintaining adaptability for atypical usage.
2Adaptability or versatility
If multiple resource management algorithms are maintained, then adaptability to different usage patterns is improved, but system complexity increases
Solution Approach 1:
A usage pattern detection module serves as an intermediary between the multiple resource management algorithms and the actual resource allocation process. This mediator detects current usage patterns and selects the appropriate algorithm, thereby managing the complexity of having multiple algorithms without requiring the system to manually configure or switch between them. The intermediary abstracts the complexity away from the core resource management functions.
Solution Approach 2:
The resource management system automatically detects usage patterns and selects appropriate algorithms without requiring external configuration or manual intervention. The system serves itself by monitoring its own usage patterns and autonomously switching between algorithms, which reduces the operational complexity despite maintaining multiple algorithms in the system.
3Productivity
If resource allocation is dynamically adjusted based on usage monitoring, then resource utilization efficiency is improved, but response time for algorithm switching increases
Solution Approach 1:
Multiple resource management algorithms are pre-loaded and ready in memory, with their parameters pre-configured for different usage scenarios. When a usage pattern change is detected, the system can immediately switch to the appropriate pre-prepared algorithm without needing to compute or configure it on-the-fly. This preliminary preparation eliminates the time penalty that would otherwise be associated with dynamic algorithm switching.
Data Source
AI summary
User intentions can be derived from observations of user actions or they can be programmatically specified by an application or component that is performing an action. The intentions can then be utilized to adjust the operation of resource managers to better suit the actions being performed by the user or application, especially if such actions are not “typical”. Resource managers can inform a centralized intention manager of environmental constraints, including constraints on the resources they manage and constraints on their operation, such as various, pre-programmed independent modes of operation optimized for differencing circumstances. The intention manager can then instruct the resource managers in accordance with these environmental constraints when the intention manager is made aware of the intentions. If no further optimization can be achieved, specified intentions may not result in directives from the intention manager to the resource managers.