Processor Unit Frequency Scaling from Predicted Network Traffic
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Solution Overview
Problem
Existing network systems lack effective power management at the computer system level, leading to inefficient power usage and increased packet drops due to processors running at default frequencies, which do not align with varying data and network traffic demands.
Innovation Solution
Implementing a system that predicts network traffic patterns using a Recurrent Neural Network (RNN) model to adjust processor unit frequency proactively, optimizing power consumption and reducing packet drops by formulating model training data from monitored metrics and executing frequency adjustments based on predicted traffic flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processor runs at default frequency, then processing capability is maintained, but power consumption increases and packet drops occur
Solution Approach 1:
The processor frequency is dynamically adjusted based on predicted network traffic patterns rather than running at a static default frequency. The system uses machine learning models to forecast traffic and proactively scales processor frequency up or down, transforming the processor from a static to a dynamic system that adapts to actual workload demands.
Solution Approach 2:
The system performs preliminary action by predicting network traffic patterns using machine learning models before the actual traffic arrives. Based on these predictions, the processor frequency is adjusted in advance, allowing the system to be prepared for upcoming workload changes rather than reacting after traffic patterns are established.
2Productivity
If processor frequency is increased, then data processing capacity improves, but power consumption increases
Solution Approach 1:
The system changes the processor frequency parameter dynamically based on predicted traffic patterns. Instead of maintaining a constant high frequency, the system adjusts the frequency parameter to match actual processing needs, reducing energy consumption when full processing capacity is not required while maintaining high capacity when needed.
Solution Approach 2:
The system implements feedback by continuously monitoring actual network traffic and comparing it with predicted patterns. This feedback loop allows the system to refine its predictions and adjust processor frequency more accurately, ensuring that processing capacity matches actual demand and avoiding unnecessary energy consumption.
3Loss of energy
If processor frequency is decreased, then power consumption reduces, but packet drops increase
Solution Approach 1:
The system performs preliminary action by predicting traffic patterns before they occur and adjusting processor frequency in advance. This proactive approach ensures that the processor is already at the appropriate frequency level when traffic arrives, preventing packet drops that would occur if frequency were reduced too low without anticipating the incoming workload.
4Loss of energy
If manual threshold-based frequency adjustment is used, then some power optimization is achieved, but system complexity and configuration difficulty increase
Solution Approach 1:
The system implements self-service by using machine learning models to automatically predict traffic patterns and determine optimal processor frequency settings without requiring manual configuration. The system learns from historical data and autonomously makes frequency adjustment decisions, eliminating the need for complex manual threshold configurations while achieving effective power optimization.
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for optimizing processor core frequency in view of predicted network traffic patterns. Network packets defining a network traffic flow can be received at a platform over time. Metrics can be derived from one or more applications executing at one or more processing units of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A processor unit frequency adjustment model can be trained using the model training data. Executing the model can be automated to adjust the frequency of a processing unit from among the one or more processing units. Additional network packets defining an additional network traffic flow can be received at a platform over time. Data contained in the additional network packets can be processed at the processing unit at the adjusted frequency.