Microserver Node Georouting for Low-Latency Edge Processing
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Solution Overview
Problem
Centralized cloud computing systems face challenges in providing optimal performance and reducing latency for users located far from the centralized cluster, especially with increasing computational demands and data generation from edge devices, leading to increased latency and bandwidth limitations.
Innovation Solution
Deploy microserver nodes in proximity to edge devices using georouting, utilizing a globally distributed edge computing platform with geohashing and blockchain validation for secure, low-latency edge applications, and integrating with cloud platforms like Google Cloud, AWS, and Microsoft Azure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized cloud computing cluster is used, then resource aggregation and management are simplified, but latency increases for users far from the centralized location
Solution Approach 1:
The patent segments the centralized cloud computing cluster into multiple distributed edge nodes positioned at different geographic locations. Each edge node operates as an independent computing unit that can process requests locally, eliminating the need for all computations to route back to a single centralized cluster. This segmentation resolves the latency issue by placing computing resources closer to users while maintaining manageable complexity through standardized node architectures.
Solution Approach 2:
The patent introduces a geographic dimension to cloud computing resource distribution. Instead of a single centralized location, computing resources are deployed across multiple physical locations worldwide. Users are routed to the nearest or most appropriate edge node based on geographic proximity, which fundamentally changes the latency dynamics by reducing the physical distance data must traverse while maintaining centralized coordination through cloud management platforms.
2Device complexity
If centralized cluster approach is used, then infrastructure management is simplified, but computational intensity and response time requirements cannot be met
Solution Approach 1:
The patent divides the monolithic centralized cluster into multiple distributed edge nodes, each capable of independently handling computational workloads. This segmentation enables parallel processing across different geographic locations, improving overall computational capacity and response times. Each edge node maintains simplified infrastructure management through standardized deployment templates and automated provisioning mechanisms.
Solution Approach 2:
The patent implements dynamic resource allocation where edge nodes can be provisioned, scaled, or deprovisioned based on real-time demand patterns. The system dynamically routes computational workloads to the most appropriate edge node based on current network conditions, user location, and node capacity. This dynamic behavior enables the system to meet varying computational intensity requirements while maintaining manageable infrastructure complexity through automation.
3Adaptability or versatility
If more robust applications with additional resources are deployed, then functionality improves, but latency and bandwidth limitations are exacerbated
Solution Approach 1:
The patent segments robust computational workloads across multiple distributed edge nodes rather than concentrating them in a single centralized cluster. This allows complex applications to execute functions locally at edge nodes, reducing the latency penalty associated with routing intensive computations to remote centralized locations. The distributed architecture maintains application robustness while improving response times through local processing capability.
Solution Approach 2:
The patent adds a distributed geographic dimension to application deployment, enabling robust applications to run across multiple physical locations simultaneously. This spatial distribution allows different components of complex applications to execute in parallel at different edge nodes, reducing overall processing time while maintaining full functionality. The system manages bandwidth by processing data locally at edge nodes rather than routing all traffic through centralized clusters.
4Loss of time
If georouting with distributed edge nodes is implemented, then latency is reduced and network throughput increases, but system complexity increases
Solution Approach 1:
The patent introduces cloud management platforms and routing services as intermediary components that coordinate the distributed edge nodes. These intermediaries handle the complexity of node discovery, load balancing, and traffic routing automatically. Edge nodes interact with these intermediary services through standardized APIs, which abstracts the underlying system complexity from individual nodes while enabling low-latency local processing. The intermediaries manage the geographic distribution aspect without requiring each edge node to independently handle routing decisions.
Data Source
AI summary
Systems and methods for locating microserver nodes in proximity to edge devices using georouting. Microservers automatically form a global peer-to-peer network to serve edge functions and content to edge devices. Edge devices use HyperText Transfer Protocol (HTTP) to execute serverless functions or otherwise retrieve data from edge nodes and/or microservers located in proximity to the HTTP client. The cloud platform locates the nearest edge node and/or microserver. Edge devices georoute HTTP requests to the nearest edge node and/or microserver. Serverless functions are implemented in secure, isolated environments using a blockchain.