Predictive Processor Power-State Control for Communication Systems
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Solution Overview
Problem
Existing power consumption control systems struggle to adapt quickly to changes in communication system operations while maintaining communication performance.
Innovation Solution
A power consumption control system that includes prediction means to forecast operation levels, power state identification to determine the lowest power consumption state for processors, and power consumption control means to operate processors in identified states based on performance index values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of operating virtual nodes is increased to suppress decrease in communication speed, then communication performance is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary prediction of future traffic patterns using machine learning models before making power state decisions. By forecasting traffic volume and characteristics in advance, the system can proactively adjust processor power states to optimize both communication performance and power consumption, rather than reacting to real-time conditions alone.
Solution Approach 2:
The system dynamically adjusts processor power states (P-states) based on predicted traffic conditions. The power state identification unit selects appropriate power states from multiple available states, and the power consumption control unit transitions processors between these states in real-time, enabling flexible adaptation to changing communication demands while optimizing the balance between performance and energy efficiency.
2Use of energy by moving object
If the number of operating virtual nodes is reduced to suppress increase in power consumption, then power consumption is improved, but communication speed decreases
Solution Approach 1:
The system uses machine learning predictions to anticipate future traffic requirements before reducing power consumption. By analyzing historical and real-time traffic data, the system can determine the optimal timing for power state transitions, ensuring that communication performance is maintained during critical periods while enabling power savings during low-demand periods.
Solution Approach 2:
The system changes processor operating parameters by transitioning between multiple power states (P-states) with different performance characteristics. The power state identification unit selects appropriate power states based on predicted traffic conditions, and the power consumption control unit executes these transitions, enabling fine-grained control over the trade-off between communication speed and power consumption.
3Adaptability or versatility
If power state transitions are made frequently to adapt to traffic changes, then adaptability is improved, but system complexity increases
Solution Approach 1:
The system reduces control complexity by performing predictions in advance. The machine learning models process historical traffic data and forecast future conditions before power state transitions are needed, allowing the control system to act on pre-computed insights rather than making real-time decisions based on complex live analysis, thus simplifying the control architecture.
Solution Approach 2:
The system introduces machine learning prediction models as intermediary components between traffic monitoring and power state control. These models act as mediators that process complex traffic patterns and output simplified power state recommendations, reducing the computational burden on the control system and making the overall system more manageable while maintaining high adaptability.
Data Source
AI summary
A power state identification module identifies, based on correspondence data indicating a correspondence between a performance index value and a degree of operation relating to at least one software element for each of a plurality of power states into which a processor is to be brought, any of power states for reaching a given target relating to the performance index value in the degree of operation being a result of prediction. A power consumption control module operates the processor configured to execute the at least one software element in identified one of the power states.