Video Analytics Profiling via Modular Resource Estimation
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Solution Overview
Problem
Deploying video analytics systems in environments with limited or intermittent network connectivity, such as stadiums, is challenging due to unpredictable resource needs, leading to labor-intensive and costly manual testing and over-deployment.
Innovation Solution
A method involving modular profiling using a directed acyclic graph representation to estimate resource usage for each functional module, allowing for efficient deployment of video analytics systems without repetitive testing, by profiling each module separately and reusing profiles across applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing and over-deployment are used to ensure system reliability in environments with limited network connectivity, then system reliability is improved, but deployment cost and complexity increase
Solution Approach 1:
The system performs preliminary profiling of functional modules to generate resource usage estimates before actual deployment. By pre-characterizing each module's resource consumption patterns under various configurations, the system can predict total system requirements without requiring manual testing or over-deployment, thus maintaining reliability while reducing deployment complexity
Solution Approach 2:
The system uses feedback from module profiling data to iteratively refine resource estimates. By collecting performance data from individual module profiles and aggregating them according to the application graph, the system continuously improves its resource prediction accuracy, enabling reliable deployment decisions without extensive manual testing
2Measurement precision
If comprehensive manual testing is performed to accurately determine resource needs, then resource estimation accuracy is improved, but time and labor requirements increase
Solution Approach 1:
The system segments the overall system profiling into individual functional module profiles. Each module is profiled separately to capture its resource usage characteristics, and these segment profiles are then composed according to the application graph structure. This segmentation enables accurate resource estimation without requiring time-consuming comprehensive system-level testing
Solution Approach 2:
The system changes parameters by profiling modules under multiple configurations (different input rates, processing modes, etc.) and storing these as parameterized profiles. By selecting and composing appropriate profile parameters based on the specific deployment scenario, the system achieves accurate resource estimation for diverse conditions without re-testing each scenario
3Reliability
If resource usage is over-estimated to ensure adequate provisioning, then system reliability is improved, but resource efficiency deteriorates
Solution Approach 1:
The system uses parameterized module profiles that capture resource usage under different operating conditions. By changing parameters based on the specific application configuration and input characteristics, the system generates accurate resource estimates tailored to each deployment scenario, avoiding both over-provisioning and under-provisioning
Solution Approach 2:
The system performs preliminary resource estimation by composing module profiles according to the application graph before deployment. This preliminary calculation provides accurate resource requirements based on the specific application configuration, enabling precise provisioning that ensures reliability without wasting resources
4Manufacturing precision
If individual module profiling is performed for each application, then customization accuracy is improved, but profiling complexity increases
Solution Approach 1:
The system segments profiling into reusable functional module profiles that can be independently created and stored in a library. Each module profile captures the resource characteristics of that specific functional unit. When deploying an application, the system segments the application into constituent modules and retrieves their pre-profiled characteristics, achieving accurate customization without repeating the entire profiling process
Solution Approach 2:
The system creates universal module profiles that can be reused across multiple different applications. A single module profile (e.g., for face detection or object recognition) can serve multiple applications by being composed differently according to the specific application graph. This universality reduces profiling complexity while maintaining accuracy through the composition mechanism
Data Source
AI summary
Methods and systems for deploying a video analytics system include determining one or more applications for a security system in an environment, including one or more constraints. Each functional module in a directed graph representation of one or more applications is profiled to generate one or more configurations for each functional module. The nodes of each graph representation represent functional modules of the respective application, and repeated module configurations are skipped. Resource usage for each of the one or more applications is estimated using the one or more configurations of each functional module and the one or more constraints. The one or more applications are deployed in the environment.