Selective Data Redundancy Elimination for Resource-Constrained Hosts
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Solution Overview
Problem
Traditional data redundancy elimination (DRE) techniques are resource-intensive and costly for resource-constrained hosts, particularly in wide area network (WAN) environments, as they require significant memory and processing power to identify redundant data elements across all content types, which is inefficient given that not all content types exhibit the same level of redundancy.
Innovation Solution
Implementing a content-type based selective data redundancy elimination (SDRE) system that selectively applies DRE only to content types with the most opportunities for redundant content identification, using a network component with a processor and storage medium to perform SDRE, which includes a packet classifier, cache manager, and end-to-end SDRE module to eliminate redundant data elements, thereby reducing computational and memory resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional data redundancy elimination (DRE) techniques are applied to all content types, then bandwidth savings are maximized, but computational and memory resources are excessively consumed on resource-constrained hosts
Solution Approach 1:
The patent segments the DRE process by introducing a packet classifier that divides incoming packets into different content types (e.g., text, images, videos, audio). Full DRE is applied only to text packets which have high redundancy, while other content types use simplified or no DRE. This segmentation resolves the contradiction by applying resource-intensive processing only where necessary, achieving bandwidth savings on text while preserving resources for resource-constrained hosts.
Solution Approach 2:
The patent applies different quality levels of DRE processing to different content types based on their redundancy characteristics. Text content receives full DRE processing (high quality) due to its high redundancy, while images, videos, and audio receive reduced or no DRE (lower quality) as they have lower redundancy and higher processing costs. This local quality differentiation resolves the contradiction by optimizing the balance between bandwidth savings and resource consumption for each content type.
2Quantity of substance
If DRE is applied to all content types, then data reduction is maximized, but processing complexity and resource requirements increase significantly
Solution Approach 1:
The patent segments the data processing pipeline into classification and selective DRE stages. The packet classifier first identifies content types, then routes packets to appropriate DRE processing levels. This segmentation reduces overall processing complexity by avoiding uniform application of complex DRE algorithms to all content types, while still achieving significant data reduction on high-redundancy text content.
Solution Approach 2:
The patent applies partial DRE action by using simplified DRE algorithms for content types with lower redundancy (images, videos, audio) while reserving full DRE capability for text content. This partial action approach achieves sufficient data reduction without the excessive processing complexity that would result from applying full DRE uniformly across all content types.
3Use of energy by moving object
If selective DRE based on content type is implemented, then resource consumption is reduced, but bandwidth savings may be compromised compared to universal DRE
Solution Approach 1:
The patent applies high-quality DRE processing locally to text content types which exhibit high redundancy patterns, ensuring maximum bandwidth savings on this dominant content type. For other content types with lower redundancy (images, videos, audio), the system applies reduced or no DRE, accepting minimal bandwidth savings in exchange for significantly reduced resource consumption. This local quality strategy resolves the contradiction by optimizing the resource-savings tradeoff for each content type based on its characteristics.
Solution Approach 2:
The patent changes the DRE processing parameter (intensity level) based on content type. Text content receives high DRE processing intensity for maximum compression, while images, videos, and audio receive low or zero DRE intensity. This parameter change approach resolves the contradiction by dynamically adjusting processing levels to match the redundancy characteristics of different content types, achieving acceptable bandwidth savings with reduced resource consumption.
Data Source
AI summary
System and method embodiments are provided for selective data redundancy elimination. In an embodiment, the method includes receiving, at a transmission point, an incoming data packet containing content, wherein the content comprises a content type, eliminating, with the transmission point, redundant data elements from the data packet when the content type matches a selective data redundancy elimination type, and bypassing, with the transmission point, selective data redundancy elimination when the content type matches a bypass-elimination type.