Markov Model Derivation from Annotated Sequence Diagrams
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Solution Overview
Problem
Current methods lack an efficient and automated way to accurately assess and analyze the impact of services on resource capacity, processing requirements, and financial implications in complex communication networks like IP Multimedia Subsystems (IMS), making it difficult for operators to plan and optimize network performance.
Innovation Solution
A method and system that utilize annotated sequence diagrams to derive input parameters for Markov models, allowing for the evaluation of service impacts on resource capacity, processing, and financial aspects, enabling comprehensive impact analysis and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to assess service impact on network resources, then flexibility and adaptability are maintained, but accuracy and efficiency deteriorate due to the complexity of complex communication networks like IMS
Solution Approach 1:
The patent segments the complex service impact assessment into distinct components by using sequence diagrams to model individual service flows and Markov models to analyze specific states and transitions. This breakdown allows each aspect of service impact (resource capacity, processing requirements, financial implications) to be evaluated separately and systematically, improving measurement precision while managing the inherent complexity of IMS networks.
Solution Approach 2:
The patent introduces sequence diagrams and Markov models as intermediary tools between the complex network services and the assessment process. These intermediaries provide a structured framework that translates complex service interactions into analyzable models, enabling accurate service impact assessment without requiring direct analysis of the entire complex network system.
2Productivity
If automated analysis methods are implemented for service impact evaluation, then efficiency and productivity improve, but the complexity of the analysis system increases
Solution Approach 1:
The patent implements self-service automation where the system automatically generates sequence diagrams from service definitions and derives Markov models from these diagrams without requiring manual intervention. This automated self-service approach significantly improves productivity by eliminating manual analysis steps while the modular architecture (sequence diagrams → Markov models → impact metrics) keeps the system complexity manageable through clear separation of concerns.
3Measurement precision
If comprehensive service modeling is performed to capture all service aspects, then measurement precision improves, but the time and resources required for analysis increase
Solution Approach 1:
The patent applies preliminary action by pre-defining the modeling framework (sequence diagrams) and analysis structure (Markov models) before conducting service impact assessments. This preparatory work establishes a reusable template that captures all necessary service aspects in a standardized format, enabling comprehensive and accurate measurements while reducing analysis time for subsequent assessments since the framework is already in place.
Solution Approach 2:
The patent utilizes parameter changes by allowing the Markov model parameters (states, transitions, probabilities) to be dynamically adjusted based on specific service characteristics. This flexibility enables the model to capture comprehensive service details when needed while allowing simplified parameter configurations for faster analysis, thus balancing measurement precision with analysis time requirements.
Data Source
AI summary
A system and method for modeling and evaluating resource requirements of services is disclosed. The system and method in one aspect enable describing an application session in a software model, for example, as a sequence of events in a sequence diagram, annotating the sequence diagram with information and using the sequence diagram and the information to create a mathematical model such as a Markov model that represents the application session. Once in Markov form the service can be analyzed. The system may include a user interface for allowing a user to enter annotations to the sequence diagram.