Adaptive Video Workload Distribution Across Processor Nodes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed video processing systems face inefficiencies due to fixed computational resources that are often overprovisioned to handle peak workloads, leading to high costs and inefficiencies, especially in applications like traffic surveillance where workloads vary significantly over time.
Innovation Solution
Implementing a management system that dynamically assigns and redistributes video analysis tasks across video processor nodes based on predictive modeling and real-time monitoring, allowing for adaptive workload distribution and task transfer between nodes based on available resources and workload thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If processing nodes are designed with computational capabilities sufficient to meet peak demand, then real-time performance is ensured during peak periods, but system cost and resource inefficiency increase
Solution Approach 1:
The patent implements dynamic workload distribution where video processing tasks are continuously reallocated across network nodes based on real-time resource availability and current workload conditions. This replaces the static overprovisioning approach with an adaptive system that adjusts computational assignments dynamically, ensuring real-time performance during peaks while avoiding waste during low-demand periods
Solution Approach 2:
The patent enables processing nodes to serve multiple functions and handle diverse video analysis tasks based on their current resource state. Nodes can dynamically take on different task types and workload levels, making the system more versatile and efficient by utilizing the same hardware resources for different purposes at different times rather than dedicating fixed capabilities to specific peak-demand scenarios
2Ease of operation
If fixed computational resources are allocated to each processing node, then task assignment simplicity is maintained, but adaptability to varying workload conditions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the management system continuously monitors resource usage and workload conditions across network nodes, then uses this information to dynamically adjust task assignments. This closed-loop control maintains operational simplicity by automating the adaptation process, allowing the system to respond to varying workload conditions without complex manual reconfiguration
Solution Approach 2:
The patent enables processing nodes to autonomously report their resource status and receive dynamic task assignments based on system-wide conditions. Each node independently participates in the workload distribution process by providing status information and accepting assignments, eliminating the need for complex centralized control while maintaining adaptability to changing conditions
Data Source
AI summary
Techniques are provided for adaptive distribution of video analysis workload over a network of video processor nodes. The nodes may include, for example, internet protocol (IP) cameras, video recorders and/or data centers. The network may also include a management system configured to assign video analysis tasks to the nodes based on the node resources and predictive modelling of the node workload. The management system may re-distribute the tasks based on performance monitoring. Some assigned tasks may be bound to the node while other tasks may be transferrable, by the node, to other nodes. The nodes may be configured to determine which of the transferrable tasks will be locally executed or transferred based on a check of resource usage against a usage policy that specifies thresholds for the determinations. The nodes may be configured to transmit video analysis packets, including image data, analysis completion status and analysis results, to other nodes.