Confidence Fabric Routing for Packet Loss and Failover Trust
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Solution Overview
Problem
Existing data delivery systems lack confidence scoring mechanisms that account for the trustworthiness and reliability of data transmitted over different communication channels, particularly in the context of failover and varying ISP connections, which can impact application reliability and performance.
Innovation Solution
A data confidence fabric (DCF) system that integrates confidence scores with packet routing logic, reflecting events like failover and channel characteristics, allowing applications to assess data trustworthiness based on communication channel trust insertion technologies and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted over multiple communication channels with failover capability, then reliability of data delivery is improved, but confidence in data trustworthiness deteriorates due to packet loss and channel variations
Solution Approach 1:
The system implements feedback by continuously monitoring communication channel conditions and packet delivery status, then using this information to dynamically adjust confidence scores. The confidence fabric collects feedback from multiple sources including packet loss rates, channel quality metrics, and delivery timing, which are then used to update the overall confidence assessment in real-time.
Solution Approach 2:
The system changes parameters by dynamically adjusting confidence scores based on varying communication conditions. When channel conditions deteriorate or packet loss occurs, the confidence parameters are modified to reflect the reduced trustworthiness. This allows the system to adapt confidence levels to match actual delivery quality without compromising the failover capability.
2Measurement precision
If confidence scoring mechanisms are integrated with packet routing logic, then data trustworthiness assessment is improved, but system complexity increases
Solution Approach 1:
The system segments the confidence scoring function into separate, modular components that can be independently implemented and managed. The confidence fabric is divided into distinct modules handling different aspects such as packet monitoring, score calculation, and routing integration. This segmentation reduces overall system complexity by allowing each component to be developed, tested, and maintained separately while still contributing to the comprehensive confidence assessment.
3Reliability
If applications require high confidence levels for data usage, then application reliability is improved, but data delivery speed deteriorates due to additional verification requirements
Solution Approach 1:
The system performs preliminary actions by pre-calculating and pre-assessing confidence scores for potential data delivery paths before actual data transmission occurs. The confidence fabric evaluates multiple routing options and their associated confidence levels in advance, allowing applications to quickly select the most reliable path without performing time-consuming verification during the actual data delivery process.
Data Source
AI summary
A data confidence fabric that is network topology aware is disclosed. When data is able to be transmitted over one of multiple communication channels, confidence information related to the transmission of the data over a selected communication channel is associated with the data. Events, such as failover, are also reflected in the confidence information and confidence score of the data. The confidence information and/or confidence scores allow applications to determine how to trust data in a manner that accounts for the transmission path.