Virtual Sensor Location Determination via Traffic Flow Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the location of sensors in a virtualized computing system are manual, time-consuming, and error-prone, especially when virtual machines with sensors are moved, requiring frequent updates to configuration files or hypervisor queries.
Innovation Solution
Analyzing captured network data from sensors to identify traffic flows and determine sensor locations relative to other sensors, automatically identifying whether a sensor is deployed on a virtual machine, hypervisor, or networking switch without additional system configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If configuration files are used to determine sensor location, then sensor deployment information can be tracked, but manual updates are required each time a sensor is deployed or migrates, making the process time-consuming and error-prone
Solution Approach 1:
The sensor automatically determines its own location by analyzing traffic flows it observes, eliminating the need for manual configuration file updates. The sensor independently identifies its deployment environment (virtual machine, hypervisor, or physical switch) through autonomous analysis of network traffic patterns.
Solution Approach 2:
The system uses feedback from observed traffic flows to continuously update sensor location information. By monitoring which traffic flows the sensor can observe, the system automatically determines and updates the sensor's deployment location without external intervention.
2Loss of information
If hypervisor-specific APIs are queried to determine sensor placement, then location information can be obtained, but the process is manually driven and time-consuming
Solution Approach 1:
The patent replaces manual querying mechanisms (hypervisor APIs) with an automated analysis system that determines sensor location by examining traffic flow patterns. This substitution eliminates the need for manual intervention and hypervisor-specific interface interactions.
Solution Approach 2:
The traffic flow analysis approach is universal and works across different hypervisor environments without requiring hypervisor-specific APIs. The same analysis mechanism can determine sensor location regardless of the underlying virtualization platform.
3Measurement precision
If manual configuration updates are performed when virtual machines migrate, then sensor location accuracy is maintained, but errors increase and manual effort is required
Solution Approach 1:
The sensor automatically tracks its own location changes by analyzing traffic flows, eliminating manual configuration updates that are prone to errors. The self-service mechanism ensures location accuracy is maintained continuously without human intervention.
Solution Approach 2:
The system continuously analyzes traffic flows to maintain accurate sensor location information, ensuring uninterrupted and error-free location tracking even during virtual machine migrations or sensor movements.
Data Source
AI summary
A virtualized computing system including software sensors captures network data from one or more traffic flows the sensors. The captured network data from a given sensor indicates one or more traffic flows detected by the given sensor. The received captured network data is analyzed to identify, for each respective sensor, a first group of sensors, a second group of sensors, and a third group of sensors. All traffic flows observed by the first group of sensors are also observed by the second group of sensors. All traffic flows observed by the second group of sensors are also observed by the third group of sensors. A location of each respective sensor relative to other sensors within the virtualized computing system is determined based upon whether the respective sensor belongs to the first group of sensors, the second group of sensors, or the third group of sensors.