Containerized Edge Compute Unit for Emergency Deployment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud computing architectures face challenges in providing low-latency, high-performance computing for real-time applications and AI/ML workloads, especially in emergency and rapid response scenarios where infrastructure may be insufficient or unavailable.
Innovation Solution
A containerized edge compute unit is designed for rapid deployment, providing a modular, self-contained data center that supports high-performance computational workloads and multiple communication modalities, enabling low-latency processing and connectivity even in remote or damaged environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data center processing is used, then data processing capacity is provided, but latency increases and real-time processing capability deteriorates
Solution Approach 1:
The patent segments the centralized data center into distributed edge computing nodes deployed at multiple locations. This segmentation allows data to be processed locally at each edge node rather than requiring all data to traverse to a central location, thereby reducing latency and improving real-time processing capability while maintaining overall processing capacity.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge nodes at different physical locations throughout the network. This dimensional change from a single centralized point to multiple distributed points enables parallel processing and reduces the distance data must travel, addressing the latency issue without sacrificing processing capacity.
2Reliability
If conventional cloud computing architecture is used, then data processing is provided, but bandwidth usage increases and data privacy concerns arise
Solution Approach 1:
The patent extracts computational processing capabilities from the centralized cloud and deploys them at edge nodes closer to data sources. This extraction allows data to be processed locally, reducing the amount of data that needs to be transmitted over the network and thereby decreasing bandwidth consumption while maintaining processing capability.
Solution Approach 2:
The patent implements local quality by enabling each edge node to process data independently based on local requirements and constraints. This allows selective processing and filtering of data at the edge, reducing the volume of data that must be transmitted to centralized locations and thus reducing overall bandwidth usage while preserving necessary processing functions.
3Reliability
If centralized data center is deployed, then computing resources are provided, but network security risks increase and availability decreases
Solution Approach 1:
The patent segments the monolithic centralized data center into multiple distributed edge nodes. This segmentation limits the impact of security breaches or failures to individual nodes rather than affecting the entire system, thereby improving overall availability and reducing network security risks through isolation of potential attack surfaces.
Solution Approach 2:
The patent introduces edge nodes as intermediary components between data sources and centralized processing. These intermediaries can perform local security functions such as authentication, encryption, and data filtering before data reaches centralized systems, thereby reducing network security risks and improving system availability through distributed security postures.
4Reliability
If existing infrastructure is used in emergency scenarios, then services can be maintained, but response capability is insufficient and services become inaccessible in damaged environments
Solution Approach 1:
The patent implements dynamic deployment of edge computing resources in response to changing emergency conditions. The system can be rapidly configured and deployed at new locations as needed, adapting to varying emergency scenarios and maintaining service accessibility where existing infrastructure has been damaged or is insufficient.
Solution Approach 2:
The patent enables self-service deployment where edge computing nodes can be autonomically configured and deployed in emergency situations without requiring extensive pre-configured infrastructure. This allows rapid establishment of computing services in damaged environments by leveraging locally available resources and enabling on-demand service activation.
Data Source
AI summary
A rapid response containerized edge data center apparatus can be deployed to an edge location associated with an emergency event, and can comprise a form factor based on a shipping container housing. One or more electrical generators and battery arrays can generate and provide electrical power to the apparatus. Server racks within the container housing can implement local inference for machine learning (ML) or artificial intelligence (AI) applications corresponding to emergency response applications or tools. The apparatus can include a local networking node to create local networks at the edge location, and a plurality of satellite transceivers for link-bonded communications and internet backhaul to one or more satellite internet constellations. A communications hub engine can be configured to provide communications relay between devices on the local networks, and to connect the devices on the local networks to remote endpoints reachable over the bonded satellite uplink and bonded satellite downlink.