Workload Policy for Proprietary Computing Tasks
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Solution Overview
Problem
Many software components and architectures fail to effectively utilize the increased performance capabilities of modern hardware in information handling systems, leading to suboptimal processing, storage, and communication of data.
Innovation Solution
A method for optimizing proprietary workloads by receiving workload simulation data, determining workload attributes, identifying hardware resources, and creating a workload policy to specify the hardware resources needed for executing computing tasks, which is then sent to the information handling system to optimize task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software components and architectures are designed for traditional hardware performance levels, then compatibility and ease of operation are maintained, but hardware performance capability is not effectively utilized
Solution Approach 1:
The patent implements dynamic workload policies that adjust hardware resource allocation based on real-time workload characteristics. The system continuously monitors workload attributes and dynamically reconfigures hardware resources (CPU frequency, memory allocation, I/O priorities) to match current computational demands, enabling the software to adapt to hardware capabilities without requiring complete architectural redesign
Solution Approach 2:
The system changes operational parameters of hardware resources based on workload analysis. By modifying parameters such as processor frequency scaling, memory bandwidth allocation, and storage I/O priorities according to workload characteristics, the system extracts maximum performance from existing hardware without increasing software architectural complexity
2Reliability
If proprietary data is used to optimize workload execution, then execution accuracy and reliability are improved, but data security and privacy protection are compromised
Solution Approach 1:
The patent creates workload simulation data that replicates the essential characteristics and patterns of proprietary workloads without containing the actual sensitive data. This simulation data includes synthetic transaction sequences, resource access patterns, and performance metrics that mirror real workload behavior, enabling optimization analysis while maintaining data security
Solution Approach 2:
The system introduces workload simulation data as an intermediary between the optimization process and proprietary data. This intermediary enables the system to analyze and optimize workload execution patterns without direct access to sensitive proprietary information, thus maintaining security while achieving execution accuracy
3Reliability
If hardware resources are over-provisioned to handle all possible workload scenarios, then service reliability is improved, but resource utilization efficiency and cost increase
Solution Approach 1:
The system implements periodic workload analysis and policy adjustment cycles. By continuously monitoring workload characteristics and periodically reconfiguring hardware resource allocation, the system ensures reliable service delivery while avoiding permanent over-provisioning. Resources are dynamically scaled up or down based on current demands rather than being statically over-provisioned
Solution Approach 2:
The patent employs dynamic resource allocation where hardware resource provisioning changes in real-time based on workload characteristics. The system transitions from static over-provisioning to dynamic adaptation, adjusting CPU, memory, and I/O resources according to actual workload demands, thereby maintaining reliability while improving utilization efficiency
Data Source
AI summary
Methods and systems for optimization of proprietary workloads involve receiving workload simulation data indicative of a proprietary computing task using proprietary information. Workload attributes are generated based on the workload simulation data without using the proprietary information. The workload attributes are used to dynamically determine a workload policy for configuration of hardware resources at an information handling system executing the proprietary computing task. After dynamic configuration of the hardware resources according to the workload policy, the proprietary computing task is executed at the information handling system.