Monte Carlo Resource Allocation for Automation Nodes
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Solution Overview
Problem
The interaction between discipline specialists in the conceptual design and software engineering phases of embedded software systems is problematic due to the use of different languages, models, and tools, leading to improper deployment defects that require debugging during verification actions, and there is a need for effective timing analysis in preemptive operating systems.
Innovation Solution
A method and system for configuring automation systems that involve generating a transition matrix and Markov chain from sequencing constraints and communication matrices to optimize resource allocation, allowing for automated timing analysis and identification of communication bottlenecks, using factor graphs and Monte Carlo simulations to predict execution times and ensure proper scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interaction between discipline specialists and software engineers is used for timing analysis, then communication between domains is possible, but time consumption and probability of errors increase
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computational methods. A processing unit automatically performs timing analysis by receiving sequencing constraints, generating transition matrices, and computing timing properties without human intervention, thereby eliminating the time-consuming manual interaction while maintaining accuracy through systematic algorithmic approaches
Solution Approach 2:
The patent introduces an intermediary processing unit that acts as a mediator between sequencing constraints and timing analysis results. This intermediary automatically generates transition matrices and computes timing properties, serving as a bridge that eliminates the need for direct manual interaction between specialists while ensuring accurate timing analysis through structured computational processes
2Reliability
If comprehensive timing analysis of all software components is conducted, then proper scheduling is ensured, but computational complexity increases
Solution Approach 1:
The patent segments the complex timing analysis problem into manageable components: receiving sequencing constraints, generating transition matrices, and computing timing properties separately. This segmentation allows the processing unit to handle each aspect independently, reducing overall computational complexity while ensuring comprehensive scheduling analysis through systematic processing of divided tasks
Solution Approach 2:
The patent transforms the timing analysis problem by changing parameters from direct analysis of all software components to analysis through transition matrices. This parameter transformation simplifies the computational complexity by representing complex inter-component timing relationships in a structured matrix format that is more efficient to process while maintaining complete scheduling coverage
Data Source
AI summary
A method including a) receiving a computer generated data set of sequencing constraints describing a software system to be executed on an automation system and including software components and runnable function entities distributed over the number of computing nodes; b) generating a transition matrix from the data set of sequencing constraints, the transition matrix having a plurality of matrix elements each of them describing, by a transition value, a transition from a runnable function entity to another runnable function entity; c) receiving a computer generated communication matrix describing communication links between the computing nodes in the automation system; d) generating a Markov chain out of the data set of sequencing constraints and the communication matrix; e) generating a distribution function from the Markov chain describing used resources of the computing nodes by the software components and runnable function entities; and f) optimizing the allocation of resources.