Portable Edge Compute Cluster for Local Data Processing
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Solution Overview
Problem
Existing data processing systems are inefficient and inflexible, requiring centralized data centers and extensive infrastructure, leading to high costs, security risks, and limited interoperability, while lacking portability and flexibility for real-time data processing.
Innovation Solution
A portable data processing system comprising a control node, subordinate compute nodes, and a network switch, capable of processing data locally via a wireless network, with heat management and security features, allowing deployment near data sources and enabling real-time insights without reliance on wide area networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is transmitted to centralized data centers for processing, then processing power and performance are improved, but network bandwidth consumption, transmission time, and security risks increase
Solution Approach 1:
The patent extracts the data processing function from centralized data centers and places it at the network edge through portable computing devices. This allows data to be processed locally where it is collected, eliminating the need to transmit data to centralized locations and thereby reducing transmission time while maintaining processing capability.
Solution Approach 2:
The patent introduces portable computing devices as intermediary processing nodes between data sources and centralized data centers. These devices perform initial data processing locally, acting as a mediator that reduces the amount of data needing transmission to centralized centers while maintaining the benefits of both local responsiveness and centralized processing power when needed.
2Weight of moving object
If rack-mount servers are used in portable form-factors, then portability is improved, but functionality and processing power decrease
Solution Approach 1:
The patent employs dynamic configuration where portable computing devices can be selectively deployed and scaled. The system allows for flexible allocation of computing resources based on immediate needs, enabling sufficient processing power to be concentrated at portable locations when required, while maintaining overall system portability through on-demand resource deployment rather than permanent fixed installations.
3Weight of moving object
If data processing equipment is deployed in portable form-factors outside climate-controlled environments, then portability is improved, but system reliability and efficiency decrease due to overheating and failure
Solution Approach 1:
The patent utilizes portable cases with integrated thermal management systems that provide climate control in a movable form-factor. These flexible enclosures incorporate cooling mechanisms that maintain appropriate temperature conditions for computing equipment while preserving portability, allowing reliable operation in diverse environmental conditions without requiring permanent climate-controlled facilities.
4Power
If experienced personnel are required to set up and maintain data processing equipment, then processing performance is improved, but ease of operation and deployment decrease
Solution Approach 1:
The patent implements automated provisioning and configuration capabilities that enable portable computing devices to be deployed with minimal human intervention. The system includes self-configuration features that automatically set up data processing pipelines, model deployment, and resource allocation, reducing the need for highly skilled personnel while maintaining high processing performance through automated optimization.
5Adaptability or versatility
If standardized data formats and protocols are implemented, then interoperability is improved, but adaptability to specific data sources decreases
Solution Approach 1:
The patent implements a universal data interface layer in portable computing devices that can handle multiple data formats and protocols. This multi-functional interface automatically adapts to different data sources while maintaining standardized internal data representations, enabling broad interoperability without requiring complex custom integration for each specific data source format.
Data Source
AI summary
Systems and methods for processing data near the point of collection are disclosed. An exemplary system includes a control node configured to execute a cluster manager and a storage manager. A plurality of subordinate compute nodes can be configured to execute tasks under the control of the cluster manager. The storage manager can be configured to manage storage of data received by the system across one or more storage devices shared by the plurality of subordinate compute nodes. Data can be received from a device proximate to the system over a local network connection. A network switch within the system can route data between the network interface, the control node, and the plurality of subordinate compute nodes. The system may be sized and configured to be carried by a single person and deployed in a variety of environments even when an Internet connection is unavailable.