SOA Security Appliance Performance Modeling via Queuing Simulation
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Solution Overview
Problem
Traditional methods for evaluating and optimizing the performance of security apparatuses in Service-Oriented Architecture (SOA) systems face challenges due to incomplete data, uncertainty in future states, and the complexity of modeling diverse vendor-specific architectures, making it difficult to predict performance and ensure mission-critical web service security.
Innovation Solution
The optimization involves generating multiple queuing performance models based on gathered data, using incremental online training and simulation, and tuning parameters of the user land, kernel, and CPU to maximize derivative points, thereby optimizing the security apparatus within the SOA system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional benchmark tests are used to evaluate security apparatus performance, then data collection is possible, but the results are limited by programming languages, compilers, hardware platforms and do not predict apparatus service levels accurately
Solution Approach 1:
The patent creates a virtual copy of the security apparatus through simulation models that replicate the behavior and performance characteristics without requiring physical hardware. This allows performance evaluation across multiple platforms without being constrained by specific hardware configurations, compilers, or programming languages, thereby improving measurement accuracy while eliminating platform dependency.
Solution Approach 2:
The simulation framework is designed to be universal and platform-independent, allowing the same performance models to evaluate security apparatus across different hardware platforms, operating systems, and configurations. This multi-functional approach enables a single evaluation system to adapt to various environments without requiring platform-specific benchmark tests.
2Loss of information
If production probing is used for exploratory actions, then performance data can be collected, but the time frame is limited and management traffic overhead increases
Solution Approach 1:
The patent performs performance evaluation through simulation models before actual production deployment. By conducting virtual experiments and data collection in advance, the system obtains comprehensive performance data without the time constraints and overhead associated with production probing. This preliminary action allows thorough evaluation while eliminating the need for time-consuming live testing.
3Measurement precision
If detailed simulation models are developed for performance evaluation, then accuracy improves, but low-level development complexity and validation requirements increase
Solution Approach 1:
The patent divides the performance evaluation system into modular components: workload models, security apparatus models, queuing network models, and performance measurement modules. Each component can be developed, validated, and maintained independently, reducing the overall development complexity while maintaining high measurement precision through the coordinated interaction of specialized sub-models.
4Ease of operation
If analytic models with abstracted algorithmic operations are used, then capacity analysis and planning become easier, but the detail level for precise performance prediction decreases
Solution Approach 1:
The patent enhances traditional analytic models by adding a temporal dimension through incremental online training mechanisms. The system starts with abstracted analytic models for easy capacity planning and progressively refines them with real-world data, transitioning from static to dynamic models. This multi-dimensional approach maintains ease of operation while improving performance prediction precision over time.
Data Source
AI summary
A device, system, and method are directed towards optimizing a security apparatus within a Service-Oriented Architecture (SOA). Performance data is gathered for the SOA. A plurality of queuing performance models are generated based on a plurality of components comprising a user land, a kernel, and a Central Processing Unit (CPU). The generation may be based on an incremental online training based on the gathered data, on a simulation based on the gathered data, on a probability distribution function, on an operational law, or the like. Derivative points are determined based on the plurality of queuing performance models. The derivative points are maximized by tuning at least one parameter of the user land, at least one parameter of the kernel, and/or at least one parameter of the CPU within at least another simulation and/or increment of the online training. The security apparatus is optimized based on the at least one parameters.