Hardware Frequency Scheduling for Lower-Power Workload Execution
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Solution Overview
Problem
Existing data processing systems face challenges in managing power consumption efficiently, leading to increased operating costs and potential downtime due to high frequency operations of hardware components, which can result in inefficient resource utilization and delayed computer-implemented services.
Innovation Solution
Optimizing the frequency of hardware components by identifying an optimized frequency for workloads based on performance metrics and criteria, generating power consumption estimates for both optimized and current configurations, and selecting the configuration that minimizes power consumption to schedule workloads efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware components operate at high frequency to improve performance, then productivity increases, but power consumption increases
Solution Approach 1:
The system dynamically changes the operating frequency parameter of hardware components based on workload requirements. By adjusting frequency from maximum to optimized levels, the system achieves necessary performance while reducing power consumption during lower-intensity operations
Solution Approach 2:
The system implements dynamic frequency adjustment rather than static operation. The hardware component frequency is modified in real-time based on actual workload demands, allowing the system to adapt between high-performance and low-power states
2Duration of action of moving object
If hardware component frequency is increased to complete workloads faster, then duration of action decreases, but power consumption increases
Solution Approach 1:
The system modifies the frequency parameter to find an optimized balance point. Rather than always using maximum frequency, the system adjusts frequency to achieve acceptable completion times while minimizing power consumption
3Productivity
If hardware components operate at maximum frequency to improve service speed, then productivity increases, but operating costs increase
Solution Approach 1:
The system changes operational parameters (frequency) to optimize the trade-off between service speed and energy cost. By operating at optimized rather than maximum frequency when appropriate, the system reduces energy loss while maintaining acceptable productivity levels
Data Source
AI summary
Methods and systems for managing a data processing system are disclosed. To manage the data processing system, a workload request may be obtained, and a type of the workload may be identified. Based on the type of the workload, an optimized frequency for a hardware component to perform the workload and a power overhead for configuring the hardware component based on the optimized frequency may be obtained. A first power consumption estimate may be obtained for performing the workload during an available time slot, and configuring the hardware component based on the optimized frequency. A second power consumption estimate may be obtained for performing the workload without configuring the hardware component. A preferred configuration for the available time slot to minimize power consumption may be identified. A data processing system and a time slot for performance of the workload may be selected in which the power consumption estimate is lowest.