Front-End Camera Resource Pooling for Distributed Video Processing
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Solution Overview
Problem
Existing distributed processing systems, such as video surveillance systems, require multiple back-end servers for complex computations, leading to increased costs, space requirements, and maintenance complexity due to the limited processing capabilities of front-end devices like IP cameras.
Innovation Solution
A service processing method and system where a master device in a resource pool assigns tasks to slave devices based on their available resources, forming a resource pool that utilizes idle resources of front-end devices, eliminating the need for separate back-end servers by dividing tasks into sub-services and assigning them to devices with sufficient capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If multiple back-end servers are deployed to handle complex computations, then the processing capability is improved, but the system structure becomes complicated and costs increase
Solution Approach 1:
The patent merges the back-end computing resources with front-end IP cameras by allowing cameras to participate in distributed computing tasks. Instead of separating computing functions into dedicated back-end servers, the system combines storage and computing functions at the edge devices (cameras), creating a unified resource pool that reduces system complexity while maintaining processing capability.
Solution Approach 2:
The patent makes front-end IP cameras multi-functional by enabling them to perform both their primary function (video capture and streaming) and secondary computing functions (participating in distributed computations). This universality eliminates the need for separate back-end servers, as the same devices perform multiple roles, thereby simplifying the overall system structure.
2Power
If multiple back-end servers are deployed to handle complex computations, then the processing capability is improved, but costs and space requirements increase
Solution Approach 1:
The patent enables front-end IP cameras to serve themselves by utilizing their idle computing resources for distributed computations. Instead of requiring external back-end servers to provide computing power, the system allows edge devices to self-contribute their unused resources, eliminating the need for additional server infrastructure and reducing both quantity and cost.
Solution Approach 2:
The patent recovers idle computing resources from front-end IP cameras that would otherwise be wasted. By harvesting and utilizing these discarded resources for distributed computations, the system eliminates the need to deploy additional back-end servers, thereby reducing the quantity of hardware required while maintaining processing capability.
3Power
If multiple back-end servers are deployed to handle complex computations, then the processing capability is improved, but maintenance complexity increases
Solution Approach 1:
The patent extracts the dedicated back-end server component from the system architecture and replaces it with a distributed computing model using existing front-end devices. By removing the separate server layer, the system eliminates the maintenance complexity associated with server deployment, monitoring, and upkeep, while still providing the necessary processing capability through distributed computations.
4Device complexity
If front-end devices are used for both capture and computation, then the number of devices is reduced, but the resource utilization efficiency needs optimization
Solution Approach 1:
The patent implements dynamic resource allocation and task scheduling in the distributed computing system. The master device dynamically assigns computation tasks to slave devices based on their current resource availability and workload, ensuring optimal utilization of computing resources. This dynamic approach allows the system to adapt to changing conditions and maximize productivity while maintaining simplified system structure.
Solution Approach 2:
The patent incorporates feedback mechanisms where slave devices report their resource status and task completion to the master device. This feedback loop enables the master device to make informed decisions about task allocation, balance workloads, and optimize resource utilization efficiency across the distributed system, ensuring high productivity despite the simplified architecture.
Data Source
AI summary
A service processing method and system, and a device to reduce a large quantity of back end servers, and to simplify a structure of a distributed system where the method includes receiving, by a master device in a resource pool, a service processing request, determining, by the master device, a resource required by a service, determining, according to a remaining resource of each slave device in the resource pool, a slave device that satisfies the resource required by the service, and assigning, by the master device, the service to the corresponding slave device for processing, where the master device and the slave device are both video surveillance front end devices, and the master device determines a device that is in the front end devices and whose remaining resource satisfies a preset threshold as the slave device.