Workload Optimizer Hardware Configuration
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Solution Overview
Problem
Existing information handling systems often fail to effectively utilize the performance capabilities of modern hardware due to suboptimal software components and architectures, leading to inefficient processing, storage, and communication of data.
Innovation Solution
A method and system for dynamically optimizing workloads by determining workload attributes and hardware resources, implementing a workload policy that configures hardware resources to match the computing task requirements, and executing tasks accordingly, utilizing a workload optimizer with policy processing, monitoring, and configuration engines to optimize hardware usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If hardware performance capability is increased, then processing speed and computational power are improved, but software components and architectures cannot effectively utilize the performance capability, leading to resource underutilization
Solution Approach 1:
The system dynamically configures hardware resources based on workload characteristics. The workload optimizer continuously monitors workload attributes and adjusts hardware configuration in real-time, transforming static hardware into dynamically adaptable resources that match task requirements, thereby resolving the mismatch between hardware capabilities and software utilization
Solution Approach 2:
The invention changes hardware configuration parameters to match workload requirements. By analyzing workload attributes and determining optimal hardware configurations, the system adjusts parameters such as resource allocation, configuration settings, and operational modes to maximize effective utilization of hardware performance capabilities
2Productivity
If workload optimization is implemented through dynamic hardware configuration, then system efficiency and performance are improved, but system complexity increases due to multiple engines and policy processing
Solution Approach 1:
The workload optimizer combines multiple functional engines (policy processing, monitoring, configuration) into a single integrated system. This merging approach maintains the sophisticated functionality needed for dynamic optimization while reducing overall system complexity compared to having separate independent components for each function
Solution Approach 2:
The workload optimizer is designed as a universal system that performs multiple functions: analyzing workload attributes, determining hardware configurations, monitoring system state, and executing configuration changes. This multi-functional design consolidates complexity into a single versatile component rather than requiring specialized systems for each task
Data Source
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AI summary
Methods and systems for optimizing workloads on information handling systems involve determining workload attributes for a computing task for execution at an information handling system. The workload attributes are used to dynamically determine a workload policy for configuration of hardware resources at the information handling system. After dynamic configuration of the hardware resources according to the workload policy, the computing task is executed at the information handling system.