Workload-Aware Processor Configuration API for Resource Scheduling
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Solution Overview
Problem
Existing job scheduling systems in data centers often fail to efficiently utilize computing resources, leading to suboptimal use of time and resources in performing jobs.
Innovation Solution
A processor management system that uses an application programming interface (API) to configure processors based on job characteristics, such as performance preferences and priorities, to optimize resource allocation and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional job scheduling systems are used, then jobs can be scheduled according to priority levels, but computing resources are not efficiently utilized
Solution Approach 1:
The system dynamically adjusts processor settings (frequency, voltage, power states) based on real-time job characteristics and system load conditions. Instead of static scheduling, the processor management application continuously monitors and reconfigures processor parameters to match current computational demands, enabling adaptive resource allocation that improves efficiency while reducing energy waste.
Solution Approach 2:
The invention changes multiple processor parameters (clock frequency, voltage levels, power consumption states, performance profiles) based on job requirements. The processor management application receives job information, determines optimal parameter configurations, and applies these changes to processors to match computational needs, thereby improving productivity while minimizing energy loss through precise parameter tuning.
2Productivity
If processor settings are not optimized for specific jobs, then scheduling is simpler, but resource allocation efficiency decreases
Solution Approach 1:
The processor management application serves as an intermediary layer between the job scheduling system and the processors. It receives job information from the scheduling system, translates requirements into specific processor configuration parameters, and applies appropriate settings. This intermediary approach maintains relative simplicity in the scheduling system while enabling sophisticated processor optimization through the specialized management application.
Solution Approach 2:
The system segments the scheduling functionality into distinct components: the job scheduling system handles high-level task allocation, while the processor management application handles detailed processor configuration. This segmentation allows each component to specialize in its function, improving overall resource allocation efficiency without significantly increasing the complexity of the core scheduling system.
3Reliability
If generic processor settings are used for all jobs, then configuration is simpler, but performance and power consumption optimization is reduced
Solution Approach 1:
The system applies local quality by configuring different processor settings for different jobs based on their specific requirements. Each job receives customized processor parameters (frequency, voltage, power state, performance profile) tailored to its computational characteristics. This localized optimization improves performance and power efficiency for each specific task while the systematic approach through the processor management application keeps overall configuration complexity manageable.
Data Source
AI summary
Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.