Predictive Resource Allocation Using Historical Data
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Solution Overview
Problem
Current computing resource management systems fail to efficiently select the most suitable resources for tasks due to oversimplification in comparing attributes, neglecting user preferences and other factors, which can lead to suboptimal resource utilization and inefficiencies.
Innovation Solution
A system that identifies alternative computing resources with lower attributes, determines a configuration parameter based on historical data or predetermined probabilities, and uses a predictive model to assess the interest in both the primary and alternative resources, enabling informed selection and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a computing resource with a higher attribute value is selected, then processing power is improved, but power consumption increases
Solution Approach 1:
The system dynamically changes the selection criteria parameters by incorporating configuration parameters that represent user preferences and historical selection patterns. Instead of simply selecting based on attribute values alone, the system adjusts the effective selection threshold by combining attribute comparison with configuration parameter weighting, allowing optimal balance between processing power and power consumption based on specific operational contexts.
Solution Approach 2:
The resource selection process transitions from a static attribute-based comparison to a dynamic predictive model that continuously adapts based on configuration parameters and historical data. The system dynamically predicts resource interest levels and adjusts selections in real-time, enabling the computing system to optimize the trade-off between processing power and power consumption according to changing operational requirements and user preferences.
2Device complexity
If simple attribute comparison is used for resource selection, then device complexity is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The system implements feedback mechanisms by utilizing historical data about resource selections and outcomes. Configuration parameters are updated based on observed user preferences and selection patterns, creating a closed-loop system where past decisions inform future selections. This feedback enables the system to learn from experience and continuously improve resource allocation efficiency without proportionally increasing system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing configuration parameters based on historical data and user preferences. Before actual resource selection occurs, the system prepares predictive models and configuration data that encode learned patterns from past selections. This preliminary preparation enables faster, more efficient real-time decision-making without adding complex computation during the critical selection moment.
3Measurement precision
If user preferences and historical data are incorporated into resource selection, then resource allocation accuracy is improved, but device complexity increases
Solution Approach 1:
The system creates simplified copies or representations of complex user preferences and historical patterns through configuration parameters. Instead of directly processing raw historical data and user preference information during resource selection, the system pre-processes this information into compact configuration parameter structures that capture essential patterns. This copying approach enables accurate preference-based selection while maintaining relatively simple real-time selection logic.
Data Source
AI summary
Resources can be managed by predicting resource usage. For example, a first computing resource having a first attribute with a first value can be identified. A second computing resource having a second attribute with a second value can also be identified. A configuration parameter can be determined based on at least one of historical data or a predetermined probability. A predetermined model can be used to predict a first amount of interest in the first computing resource based on the first value, the second value, and the configuration parameter. The predetermined model can also be used to predict a second amount of interest for the second computing resource based on the first value, the second value, and the configuration parameter. Information related to the predicted first amount of interest in the first computing resource and the predicted second amount of interest in the second computing resource can be transmitted.