Traffic Pattern Cost Prediction for Cloud Firewall Deployment
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Solution Overview
Problem
Cloud service providers face challenges in optimizing the deployment of cloud firewalls to balance resource provisioning with cost management while ensuring quality service without unacceptable latencies, as they are unaware of the optimal quantity and size of firewalls needed to minimize costs and maintain sufficient availability.
Innovation Solution
A system that utilizes a machine learning model, trained on traffic log data and CPU utilization data, to predict usage costs based on network traffic patterns, enabling informed decisions on the optimal deployment of cloud firewalls to minimize costs while maintaining service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud firewalls are deployed with sufficient resource provisioning to ensure quality service and acceptable latencies, then service reliability is improved, but deployment costs increase
Solution Approach 1:
The patent implements dynamic firewall resource provisioning that automatically adjusts firewall quantity and size based on real-time traffic pattern analysis. The system transitions from static over-provisioning to dynamic adaptation, where firewall resources are scaled up during high-traffic periods and scaled down during low-traffic periods, resolving the contradiction between maintaining service reliability and reducing deployment costs.
Solution Approach 2:
The system changes key parameters including traffic pattern recognition thresholds, firewall resource allocation ratios, and scaling trigger points. By continuously monitoring and adjusting these parameters based on learned traffic patterns, the system optimizes the balance between service availability and resource consumption, preventing both over-provisioning and under-provisioning.
2Quantity of substance
If cloud firewalls are deployed with minimal resource provisioning to reduce costs, then deployment costs decrease, but service quality and latencies deteriorate
Solution Approach 1:
The system performs preliminary analysis of traffic patterns using machine learning models to predict future traffic demands. By anticipating traffic surges before they occur, the system can proactively provision additional firewall resources in advance, ensuring service quality is maintained during peak periods while avoiding the need for constant over-provisioning.
Solution Approach 2:
The system implements continuous feedback loops that monitor service quality metrics including latency and throughput. When degradation is detected, the system automatically triggers resource scaling actions. This feedback mechanism ensures service quality thresholds are maintained while minimizing resource consumption during normal operating conditions.
Data Source
AI summary
Traffic log data generated by cloud firewalls executing in a cloud environment during a time period that indicate classes and corresponding amounts of network traffic detected across sessions as well as usage cost data recorded for the cloud firewalls during the time period are obtained. The traffic log data are preprocessed to generate training data comprising feature vectors indicating the aggregate amount of network traffic detected for each traffic class during a corresponding time interval within the time period and are labeled with the associated usage cost. A machine learning model is trained on the labeled traffic log data to learn the impact each traffic class has on the accumulated usage costs. The trained model generates predicted usage costs based on distributions of detected network traffic across traffic classes that are analyzed to correlate traffic patterns with usage costs to determine the optimal size(s) of cloud firewalls to deploy.