CV Node Leader Distributes Workloads to Candidate Nodes
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Solution Overview
Problem
In computer vision environments, computing devices face inefficiencies in performing workloads due to the need for acquiring additional data for computer vision alerts, which can impact performance and require distributed workloads that may not effectively utilize available resources.
Innovation Solution
A method where a CV node identifies alerts, determines if it's not participating in a higher priority workload, selects candidate nodes, initiates a distributed workload to generate data, and updates alert cases, improving resource utilization and efficiency by selecting nodes based on availability and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a computing device performs computer vision workloads and acquires additional data for CV alerts, then the completeness of alert resolution is improved, but the performance of ongoing computer vision workloads deteriorates
Solution Approach 1:
The system segments the computer vision environment into multiple independent CV nodes that can operate autonomously. When a CV alert is generated, the workload is segmented and distributed to specific candidate CV nodes based on their availability and relevance, rather than impacting the entire system. This allows alert resolution to proceed without degrading the performance of ongoing workloads on other nodes.
Solution Approach 2:
CV nodes are designed with multi-functionality, serving both as primary processors for ongoing workloads and as candidate nodes for distributed alert resolution workloads. Each CV node can dynamically switch roles based on system needs, allowing the same resource pool to handle both continuous monitoring tasks and alert investigation tasks without requiring dedicated separate infrastructure.
2Productivity
If a distributed workload is initiated to acquire additional data for CV alerts, then the efficiency of data acquisition is improved, but the complexity of coordinating multiple CV nodes deteriorates
Solution Approach 1:
The system implements self-service mechanisms where CV nodes autonomously determine their own availability status and selectively participate in distributed workloads based on their current load and capabilities. The leader CV node simply needs to identify suitable candidates and initiate the workload, while the candidate nodes self-manage their participation and data generation, eliminating the need for complex centralized coordination protocols.
Solution Approach 2:
CV nodes provide feedback information about their availability, current workload status, and relevance to alert resolution. The leader CV node uses this feedback to make intelligent selection decisions about which nodes to recruit for distributed workloads. This feedback mechanism enables efficient coordination without requiring complex centralized control, as each node contributes information that guides system-wide decisions.
3Reliability
If candidate CV nodes are selected based on availability and relevance, then the effectiveness of distributed workload performance is improved, but the complexity of node selection criteria deteriorates
Solution Approach 1:
CV nodes pre-report their availability status, capabilities, and relevant parameters to the system before being needed for alert resolution. This preliminary information gathering allows the leader CV node to quickly assess which nodes are suitable candidates without needing to perform complex real-time evaluations. The selection criteria are effectively pre-computed and stored, simplifying the decision-making process when alerts occur.
Data Source
AI summary
Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; in response to identifying the CV alert: making a first determination that the CV node is not participating in a distributed workload associated with a higher priority CV alert; in response to the first determination, the CV node: selects candidate CV nodes of the plurality of CV nodes; initiates performance of the distributed CV workload by the candidate CV nodes to generate CV data associated with the CV alert; generates a CV alert case associated with the CV alert; obtains CV data from the candidate CV nodes that are performing the distributed CV workload; updates the CV alert case using the CV data generated during the performance of the distributed CV workload; and provides the updated CV alert case to a VMS.