Vehicle Network Logger Data Tiering for Event-Based Storage
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Solution Overview
Problem
Existing vehicle network logger systems face inefficiencies in data management, including excessive storage requirements, inefficient data offloading, and challenges in detecting and recording events of interest in real-time, leading to increased costs and latency.
Innovation Solution
Implementing a classification technique for vehicle network logger systems that prioritizes mandatory data logging and retention while opportunistically logging non-mandatory data based on disk space availability and dynamic data campaigns, using data tiers and triggers to manage data collection needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all sensor data is logged continuously to ensure complete data availability for analysis and compliance, then data completeness is improved, but storage requirements and system costs increase significantly
Solution Approach 1:
The patent applies local quality by implementing data tiering where different portions of the logging system handle different data types with different retention and priority levels. Critical safety data is logged with high priority and retained longer, while non-critical data is logged with lower priority and can be overwritten sooner, optimizing storage utilization based on data importance rather than treating all data uniformly
Solution Approach 2:
The patent segments the logging system into multiple data tiers (e.g., tiered logging with different priorities) and uses event-triggered logging to divide continuous logging into discrete event-based segments. This segmentation allows the system to focus storage resources on important events while reducing overall storage requirements compared to unconditional continuous logging
2Measurement precision
If continuous logging of all data is performed to capture every event for analysis, then detection accuracy is improved, but processing time and system latency increase
Solution Approach 1:
The patent implements periodic action through event-triggered logging where instead of continuous logging, the system periodically activates logging based on detected events or conditions. This allows the system to maintain high detection accuracy for critical events while reducing overall processing time and computational load by not continuously processing all sensor data streams
Solution Approach 2:
The patent applies dynamics by making the logging rate and priority adjustable based on system state and event importance. The logging system dynamically adapts its behavior - increasing logging intensity for critical events and reducing it for normal operation - thereby maintaining detection accuracy when needed while minimizing processing time during routine operations
3Reliability
If high-priority mandatory data is always retained to ensure compliance and safety analysis, then data reliability is improved, but storage capacity is consumed faster
Solution Approach 1:
The patent applies parameter changes by implementing data tiering with different retention policies for different data priorities. Mandatory high-priority data is retained with high reliability and longer duration, while non-mandatory data has adjustable retention parameters. This allows the system to maintain high reliability for critical compliance data while managing overall storage capacity through differentiated retention strategies
4Quantity of substance
If all logged data is offloaded to cloud storage to reduce on-vehicle storage requirements, then storage management is improved, but offload time and network bandwidth consumption increase
Solution Approach 1:
The patent applies the taking out principle by selectively extracting and offloading only specific portions of data to cloud storage based on priority and size. Rather than offloading all logged data uniformly, the system extracts high-priority or large-volume data for cloud storage while retaining smaller, time-critical data locally, thereby reducing offload time and network bandwidth consumption while still achieving storage management goals
Data Source
AI summary
A vehicle network logging system (VNLS) is described and includes a logger for storing data associated with events, wherein for each of the events, the associated data is created when a trigger associated with the event is received at the VNLS; and a compute node for, for each of the events, subsequent to occurrence of creation of data associated with the event, identifying a data tier for the event, the identified data tier selected from a plurality of data tiers of the VNLS and defined by the trigger associated with the event; and logging the data for the event to an internal disk of the VNLS in the identified data tier, wherein the identified data tier specifies an offload policy and a deletion policy for the data.