Dynamic Control System for Computing Input Parameter Optimization
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Solution Overview
Problem
Configuring application-level and system-level parameters for complex IT systems to meet performance metrics and quality of service targets is challenging due to the large number of tunable parameters and complex interrelationships, leading to inefficient resource allocation and potential overload or underutilization as workloads fluctuate.
Innovation Solution
A control system that dynamically selects and adjusts a subset of critical knobs using online dimension reduction and machine learning techniques, such as LASSO, to identify and adjust the most influential parameters, thereby achieving desired performance metrics and quality of service targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If statically allocated resources are used, then system configuration is simple, but resource utilization is inefficient and systems are either underutilized or overloaded
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring system workload and automatically adjusting resource allocation parameters in real-time, transitioning from static to dynamic configuration to optimize resource utilization while maintaining manageable system complexity through automated control
2Adaptability or versatility
If human operators manually configure parameters, then flexibility to meet performance metrics is improved, but operational complexity and error-proneness increase
Solution Approach 1:
The system performs self-configuration by automatically monitoring performance metrics, identifying which parameters need adjustment, and modifying configuration parameters autonomously without human intervention, thereby maintaining adaptability to performance requirements while eliminating manual operational complexity
Solution Approach 2:
The patent implements closed-loop feedback control where system performance is continuously monitored and fed back to automatically adjust configuration parameters, enabling the system to adapt to changing conditions while removing the burden of manual configuration from operators
3Productivity
If all tunable parameters are adjusted, then comprehensive optimization is achieved, but system complexity and difficulty of identification increase
Solution Approach 1:
The patent extracts and identifies only the critical subset of parameters that have the most significant impact on performance metrics, ignoring less important parameters, thereby achieving effective optimization with reduced complexity by focusing on the essential few rather than all possible parameters
4Speed
If real-time parameter adjustment is implemented, then responsiveness to workload changes is improved, but control complexity increases
Solution Approach 1:
The patent implements automated feedback control systems that continuously monitor workload changes and automatically adjust parameters in real-time, achieving rapid response to changing conditions while managing control complexity through systematic automated control algorithms rather than manual intervention
Data Source
AI summary
A technique for controlling an output of a computing system having multiple adjustable inputs includes providing a set of adjustable inputs to the computing system, observing an output of the computing system while the system is in operation, and selecting a subset of adjustable inputs from the set of adjustable inputs based on the observation of the output. The inputs in the selected subset are then adjusted to achieve a desired output of the computing system.