Predictive Resource Scaling for Network Traffic and Power Efficiency
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Solution Overview
Problem
Existing resource scaling approaches in data-intensive and network-intensive applications fail to optimize power usage efficiency and packet loss by not predicting network traffic patterns and power consumption, leading to inefficient resource utilization and potential data loss.
Innovation Solution
Utilizing a Recurrent Neural Network (RNN) model, such as a Long Short-Term Memory (LSTM), to predict future network traffic patterns and adjust resource allocation (vertical and horizontal scaling) to optimize resource utilization based on historical Key Performance Indicators (KPIs), minimizing packet drops and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more resources are allocated to handle network traffic, then packet loss is reduced, but power consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future network traffic patterns using historical data and machine learning models. Resource allocation is adjusted in advance based on predicted traffic, allowing the system to prepare adequate resources before traffic spikes occur, thereby preventing packet loss while avoiding the need to maintain excessive resources continuously and reducing overall power consumption.
Solution Approach 2:
The system implements dynamic resource allocation that continuously adapts to changing network conditions. Instead of static resource allocation, the system dynamically adjusts resource levels based on real-time traffic monitoring and predictions, enabling optimal matching of resources to actual demand and resolving the contradiction between maintaining sufficient resources for reliability and reducing power consumption.
2Use of energy by moving object
If fewer resources are allocated to reduce power consumption, then power efficiency improves, but packet loss increases
Solution Approach 1:
The system performs preliminary actions by predicting future network traffic patterns using historical data and machine learning models. Resource allocation is adjusted in advance based on predicted traffic, allowing the system to prepare adequate resources before traffic spikes occur, thereby preventing packet loss while avoiding the need to maintain excessive resources continuously and reducing overall power consumption.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual network traffic and compare it with predictions. This feedback loop allows the system to learn from past performance and improve future predictions, ensuring that resource allocation decisions are optimized to prevent packet loss while maintaining power efficiency. The feedback also enables the system to correct any deviations from optimal resource allocation in real-time.
3Productivity
If resource allocation is adjusted manually based on current traffic, then resource utilization improves, but response time to traffic changes increases
Solution Approach 1:
The system performs preliminary actions by predicting future network traffic patterns using historical data and machine learning models. Resource allocation is adjusted in advance based on predicted traffic, allowing the system to prepare adequate resources before traffic spikes occur, thereby preventing packet loss while avoiding the need to maintain excessive resources continuously and reducing overall power consumption.
Solution Approach 2:
The system replaces manual resource allocation mechanisms with automated machine learning-based prediction and adjustment systems. This substitution eliminates the delays associated with manual monitoring and decision-making, enabling the system to automatically detect traffic patterns and adjust resource allocation in real-time, thereby improving both resource utilization and response time to traffic changes.
4Ease of operation
If resources are scaled reactively after traffic changes, then resource maintenance is simplified, but packet drops occur during transition
Solution Approach 1:
The system performs preliminary actions by predicting future network traffic patterns using historical data and machine learning models. Resource allocation is adjusted in advance based on predicted traffic, allowing the system to prepare adequate resources before traffic spikes occur, thereby preventing packet loss while avoiding the need to maintain excessive resources continuously and reducing overall power consumption.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual network traffic and compare it with predictions. This feedback loop allows the system to learn from past performance and improve future predictions, ensuring that resource allocation decisions are optimized to prevent packet loss while maintaining power efficiency. The feedback also enables the system to correct any deviations from optimal resource allocation in real-time.
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for optimizing resource allocation in view of predicted network traffic patterns and predicted power consumption. 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 on resources of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A resource adjustment model can be trained using the model training data. Executing the model can be automated to adjust resource allocation at the platform. 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 using the adjusted resource allocation.