Edge Compute System Latency Reduction via Intermediary Segmentation
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Solution Overview
Problem
Traditional client-server architectures face challenges with latency-sensitive and computationally intensive tasks due to high latency in data communications and limited client device resources, making it difficult to offload such tasks effectively.
Innovation Solution
Implementing an edge compute system with a distributed data-processing architecture that offloads latency-sensitive tasks to edge computing devices, utilizing a speed layer at the edge of the network to reduce latency and enhance performance, while maintaining a batch layer in the cloud for data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If tasks are offloaded to a traditional cloud server, then computing resources are increased, but data communication latency increases
Solution Approach 1:
The patent introduces an edge computing device as an intermediary between the client device and the traditional cloud server. This edge device is positioned geographically closer to the client, serving as a mediator that handles latency-sensitive tasks locally while maintaining connection to the broader cloud infrastructure, thus reducing communication latency without sacrificing computing resource access
Solution Approach 2:
The patent segments the computing architecture into multiple layers: edge computing devices positioned at network edges for latency-sensitive tasks, and traditional cloud servers for computationally intensive but less time-critical tasks. This segmentation allows different types of workloads to be processed at optimally positioned infrastructure points
2Loss of time
If tasks are performed on the client device, then data communication latency is reduced, but computing resources are insufficient
Solution Approach 1:
The edge computing device acts as an intermediary that extends the client device's computing capabilities. Instead of the client device attempting to handle all tasks locally with insufficient resources, the edge device provides additional computing power while maintaining low latency through proximate physical location
3Quantity of substance
If a traditional client-server architecture is scaled to virtualize client device compute, then computing resources are increased, but the scaling is linear and not economical
Solution Approach 1:
The patent applies local quality by deploying computing resources at specific strategic locations (edge of the network closer to clients) rather than uniformly scaling traditional cloud infrastructure. This localized deployment of edge computing devices provides targeted computing enhancement where it is most needed, avoiding linear scaling of entire infrastructure
Data Source
AI summary
An exemplary system includes a cloud server configured to provide a service to a client device by way of a network. The system further includes an edge computing device configured to provide, from an edge of the network to the client device, a latency-sensitive task associated with the service. The cloud server and the edge computing device are configured to operate on distinct and matching datasets to provide the service to the client device.