Latency Band Graphs for Aperiodic Traffic Estimation
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Solution Overview
Problem
Current scheduling models fail to efficiently support both periodic and aperiodic message transmissions on common hardware, leading to over-engineered systems and unpredictable latency for critical and non-critical applications, with no effective way to predict aperiodic message latencies, especially in complex multi-class traffic systems.
Innovation Solution
The technique involves binning aperiodic latency sample data using latency band graphs and fluid flow analysis to generate a small, fixed set of automatically determined bins, which provides a compact representation for real-time latency estimation, dependent only on periodic message traffic, and accounts for the patterns in periodic timeline gaps that affect latency distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sufficient bandwidth is statically reserved for worst case event arrival, then reliability of critical aperiodic functions is improved, but device complexity and hardware costs increase due to over-engineering
Solution Approach 1:
The patent transitions from static bandwidth reservation to dynamic bandwidth allocation. The system dynamically adjusts the number of bins and latency estimation parameters based on actual traffic patterns, allowing the system to adapt to varying workload conditions without over-engineering for worst-case scenarios. This dynamic approach maintains reliability while reducing hardware complexity.
Solution Approach 2:
The patent changes the parameter of bandwidth reservation from fixed static allocation to variable dynamic allocation. By adjusting the number of bins and latency thresholds based on observed traffic characteristics, the system optimizes resource utilization without compromising reliability, thereby reducing overall hardware complexity and costs.
2Measurement precision
If empirical distribution functions are constructed using large samples, then measurement precision of latency distributions is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the latency distribution into a manageable number of bins rather than using continuous empirical distribution functions. This segmentation reduces the amount of data processing required while maintaining sufficient precision for latency estimation, thereby reducing device complexity and processing requirements.
Solution Approach 2:
The patent extracts key latency statistics from large empirical datasets and stores them in a compact form factor. By extracting and pre-computing essential latency characteristics, the system achieves high measurement precision without the need to process and store entire large datasets, thus reducing device complexity.
3Reliability
If static scheduling techniques are used for periodic functions, then reliability of closed loop control is improved, but adaptability to aperiodic message transmission deteriorates
Solution Approach 1:
The patent introduces dynamic elements to the static scheduling framework. By dynamically adjusting scheduling parameters and bandwidth allocation based on actual traffic patterns, the system maintains the reliability of periodic control while gaining adaptability to handle aperiodic message transmission effectively.
Solution Approach 2:
The patent creates a universal scheduling framework that can handle both periodic and aperiodic functions using the same infrastructure. The system uses a unified approach with configurable parameters that work for different traffic types, eliminating the need for separate specialized systems and improving overall adaptability.
Data Source
AI summary
A technique for binning aperiodic latency sample data using a data representation called latency band graphs. A fluid flow analysis produces a small, fixed size set of automatically generated bins dependent only on the timeline defined by periodic traffic. The compact number of bins yields a parameterized latency representation suitable for real-time estimation and goodness-of-fit tests.