Load Balancing Computer for Vehicle Data Aggregation
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Solution Overview
Problem
The increasing volume of vehicle status reports from telematics-equipped vehicles has overwhelmed traditional data processing techniques, rendering them inadequate for handling the vast amounts of big data generated, necessitating an efficient method for data aggregation.
Innovation Solution
A private computer network with a load balancing computer (LBC) and a telematics server farm is used to receive, parse, and aggregate vehicle data, where the LBC determines the appropriate telematics server for message processing based on unencrypted data, and reroutes messages if the selected server is experiencing a processing delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing techniques are used to handle vehicle status reports, then data processing can be performed with simple infrastructure, but the system becomes inadequate for handling the vast volumes of big data generated by increasing numbers of telematics-equipped vehicles
Solution Approach 1:
The system segments the data processing infrastructure into multiple telematics servers distributed across a network, with a load balancing computer coordinating them. This segmentation allows the system to handle vast volumes of big data by distributing the processing load across multiple servers rather than relying on a single traditional processing system.
Solution Approach 2:
The load balancing computer acts as an intermediary between subscriber vehicles and the telematics server farm. It receives upload messages, parses unencrypted data to determine affinity, and intelligently routes messages to appropriate servers. This intermediary component enables the system to manage complex data traffic patterns while maintaining scalability.
2Productivity
If a single telematics server processes all upload messages, then the system structure remains simple, but processing delays occur when the server is overwhelmed by large volumes of data
Solution Approach 1:
The system divides the processing workload across multiple telematics servers instead of using a single server. Each server handles a portion of the upload messages, which eliminates processing bottlenecks and reduces delays even during high-volume data transmission periods.
Solution Approach 2:
The load balancing computer dynamically selects which telematics server receives each upload message based on real-time conditions and affinity data. This dynamic routing optimizes processing throughput by directing traffic to available servers and preventing any single server from becoming overwhelmed.
3Ease of operation
If all upload messages are routed to the same telematics server, then message aggregation is simplified, but system performance degrades when that server experiences processing delays
Solution Approach 1:
The system segments the telematics server functionality across multiple servers in a farm configuration. Each server can independently aggregate messages for its assigned vehicles, maintaining aggregation simplicity while distributing the workload to preserve system performance during high-volume operations.
Solution Approach 2:
Multiple telematics servers provide universal message aggregation capabilities, meaning any server can handle aggregation for any vehicle. This multi-functionality allows the load balancing computer to distribute messages across servers based on performance conditions while maintaining the same aggregation functionality, thus preserving both simplicity and performance.
4Reliability
If the system processes encrypted data to determine server routing, then data security is maintained throughout, but processing time increases due to decryption requirements
Solution Approach 1:
The system extracts only the necessary unencrypted data elements from upload messages for routing decisions. By parsing and using only the unencrypted portions of messages to determine affinity and select servers, the system avoids the time-consuming decryption process while maintaining data security for the encrypted portions.
Solution Approach 2:
The system performs preliminary parsing of unencrypted data elements before any decryption operations. By determining server affinity and routing decisions based on already-available unencrypted data, the system eliminates unnecessary decryption steps and reduces overall processing time while maintaining security protocols.
Data Source
AI summary
A private computer network associated with a vehicle data service center and a method of aggregating vehicle data using the private computer network. The method includes the steps of: receiving from a first subscriber vehicle one or more upload messages at a load balancing computer (LBC), wherein each of the one or more upload messages comprise unencrypted data; based on the unencrypted data, selecting at the LBC at least one telematics server of a telematics server farm to send the one or more upload messages; and providing the one or more upload messages to the at least one telematics server for upload message aggregation, wherein the LBC and the telematics server farm are associated with the private computer network.