Aggregate Power Display for Data Processing Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face difficulties in synchronizing power consumption measurements across numerous data processing systems due to limitations in polling multiple systems simultaneously and variations in power measurement intervals, making it challenging to accurately report aggregate power consumption.
Innovation Solution
A method that receives and organizes power consumption files from data processing systems with unique time intervals, sums power consumption for selected time units, and displays aggregate power consumption over a specified reporting period, handling missing data points by recording events for non-reporting systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronized measurement polling is used for data processing systems, then measurement precision is improved, but device complexity increases and productivity decreases due to thread limitations and coordination overhead
Solution Approach 1:
The system performs preliminary actions by having each data processing system autonomously record its power consumption data in local files with timestamps before any aggregation occurs. This eliminates the need for complex synchronized polling, as data is already captured and ready for retrieval, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
Each data processing system independently records its own power consumption data without requiring external polling coordination. The systems self-manage their measurement intervals and data storage, eliminating the need for complex multi-threaded polling mechanisms and reducing overall system complexity while preserving accurate power measurement capabilities.
2Measurement precision
If synchronized measurement polling is implemented across multiple data processing systems, then aggregate power consumption accuracy is improved, but productivity decreases due to limitations in simultaneously executing polling threads
Solution Approach 1:
Power consumption data is recorded in advance by each system independently with timestamps, so that aggregate reporting can be performed efficiently without requiring simultaneous polling of all systems. The aggregation process simply retrieves and sums pre-recorded data from different time intervals, dramatically improving productivity while maintaining accurate aggregate power consumption measurement.
Solution Approach 2:
Each data processing system autonomously monitors and records its own power consumption at its own measurement intervals, eliminating the need for a centralized polling mechanism that would be limited by thread execution constraints. This self-service approach enables scalable aggregation across many systems without productivity penalties.
3Adaptability or versatility
If different data processing systems use different power measurement intervals, then adaptability is improved, but measurement precision deteriorates due to inability to synchronize measurements
Solution Approach 1:
Each system records power consumption data in advance with precise timestamps indicating when measurements were taken. This preliminary recording with temporal metadata allows the aggregation system to accurately correlate data from different measurement intervals, maintaining measurement precision while accommodating diverse system adaptability and different polling frequencies.
Solution Approach 2:
The system adds a time dimension to the aggregation process by using timestamps to organize and correlate power consumption data from systems with different measurement intervals. Instead of requiring spatial synchronization (simultaneous measurements), the solution uses temporal ordering to achieve accurate aggregate measurements, thereby preserving both adaptability and precision.
Data Source
AI summary
The present invention provides a method for providing aggregate power consumption information for hundreds or thousands of data processing systems interconnected in a network. In one embodiment, a method is provided that receives power consumption information for each of the data processing systems along with time intervals associated with this information. The power consumption information is analyzed to provide the power consumption of the total number of data processing systems. This is accomplished by selectively computing total power consumption for selected time values.