Networked Image Processing Load Sharing via Peer Delegation
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Solution Overview
Problem
In networked office environments, image processing devices often experience uneven workload distribution, with some devices heavily loaded while others are idle, leading to inefficiencies in processing large color documents without the cost-effectiveness of multiple CPU approaches.
Innovation Solution
A method and system for dynamically sharing image processing loads among interconnected devices, allowing a target device to request and delegate image processing tasks to idle peers via a network, without the need for a server or centralized management, using embedded web servers and Unix-like operating systems to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image processing tasks are concentrated on a single target device, then the device can maintain full control over job processing and output delivery, but the device becomes heavily loaded and processing speed decreases
Solution Approach 1:
The patent divides the image processing workload into separate segments: the target device handles job management and rendering, while peer devices handle intensive image processing tasks. This segmentation distributes the computational burden across multiple devices, improving processing speed without compromising job control.
Solution Approach 2:
The patent introduces an intermediary communication mechanism where the target device sends processing requests to peer devices and receives processed data back. This intermediary approach allows the target device to maintain control over job processing while leveraging external processing power from idle peer devices.
2Productivity
If multiple CPU approaches are used to handle large color documents, then processing capacity increases, but the system becomes more complex and less cost-effective for networked office machines
Solution Approach 1:
The patent makes idle peer devices serve multiple functions: they perform image processing tasks when requested while maintaining their primary functions. This multi-functionality increases processing capacity without requiring dedicated processing hardware, avoiding the complexity and cost of multiple CPU configurations.
Solution Approach 2:
The patent enables peer devices to automatically respond to processing requests from target devices without human intervention. The system self-manages workload distribution by having idle devices volunteer their processing power, eliminating the need for complex centralized management while increasing processing capacity.
3Productivity
If image processing is delegated to peer devices, then the target device can process jobs faster by utilizing idle resources, but network traffic increases due to data transfer
Solution Approach 1:
The patent performs preliminary actions by having the target device prepare and send only the necessary processing requests and essential job data to peer devices, rather than transferring complete high-resolution images. This preliminary preparation minimizes network traffic while maintaining high throughput by processing what is needed efficiently.
Data Source
AI summary
A networked environment for sharing an image processing load for a rendering job and associated method is disclosed. In one embodiment, the networked environment includes: an image source device, a first image processing device, a network for data communications, and a second image processing device. The second image processing device is spaced apart from the first image processing device. The first image processing device includes a first image processing resource and a rendering resource. The second image processing device includes a second image processing resource. The first image processing device receives a rendering job from the image source device for rendering. The rendering job requires image processing prior to such rendering. A portion of the image processing required for the rendering job is performed by the second image processing resource. The rendering resource renders the rendering job when image processing is complete.