Hierarchical Caching for Low Latency IoT Analytics
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Solution Overview
Problem
Conventional data center-based processing solutions face high latency and bandwidth issues with large data sets, making it difficult to perform real-time analytics and respond quickly to users, especially in IoT applications, which are exacerbated by data congestion and security concerns as IoT devices proliferate.
Innovation Solution
A multi-stage hierarchical caching and analytics system with edge nodes and an edge cloud video headend that utilizes flexible computing with integrated wireless and photonics links to enable low latency communication and real-time decision-making, supporting IoT applications and handling big unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is sent to a central data warehouse for processing, then data storage capacity is improved, but latency and response time increase
Solution Approach 1:
The patent segments the centralized data processing architecture into a hierarchical structure with edge nodes, regional data centers, and cloud data warehouses. Edge nodes perform local processing and caching, regional data centers handle intermediate processing, and cloud data warehouses provide bulk storage. This segmentation enables data to be processed closer to its source, reducing latency while maintaining storage capacity at higher levels.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by distributing compute and storage resources across multiple geographic locations rather than concentrating them in a single central data warehouse. This multi-dimensional architecture allows data to be processed at the edge of the network, reducing the distance data must travel and thereby reducing latency while preserving storage capacity at centralized locations.
2Power
If data is sent to the cloud for analysis, then data processing power is improved, but bandwidth consumption and security risks increase
Solution Approach 1:
The patent implements preliminary action by performing data processing and analytics at the edge nodes before data is transmitted to the cloud. Edge nodes execute analytics functions locally, filtering and preprocessing data to extract only essential information for cloud transmission. This reduces bandwidth consumption by minimizing the volume of data sent to the cloud while maintaining processing power through distributed edge compute resources.
Solution Approach 2:
The patent applies local quality by enabling edge nodes to perform data processing and analytics locally rather than requiring all data to be transmitted to centralized cloud resources. This local processing capability reduces bandwidth consumption by handling data at its source and improves security by minimizing data transmission across the network while still providing access to processing power through edge compute resources.
3Quantity of substance
If conventional data center processing is used, then data storage capacity is improved, but real-time analytics capability deteriorates
Solution Approach 1:
The patent segments the data processing function into real-time analytics at edge nodes and batch processing at centralized data centers. Edge nodes perform real-time analytics on incoming data streams, providing immediate insights, while centralized data centers handle bulk storage and historical analysis. This segmentation enables real-time analytics capability at the edge while preserving storage capacity at centralized locations.
Solution Approach 2:
The patent introduces edge nodes as intermediaries between data sources and centralized data centers. These edge nodes perform real-time analytics and filtering, acting as a mediator that processes data locally before transmission to the cloud. This intermediary function enables real-time analytics capability while reducing the burden on centralized data centers, allowing them to focus on storage and historical analysis.
Data Source
AI summary
A system includes at least one end-node, at least one edge node, and an edge cloud video headend. The at least one end node generally implements a first stage of a multi-stage hierarchical analytics and caching technique. The at least one edge node generally implements a second stage of the multi-stage hierarchical analytics and caching technique. The edge cloud video headend generally implements a third stage of the multi-stage hierarchical analytics and caching technique.