State-Based Observability Traffic Simulation Without Dummy Infrastructure
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Solution Overview
Problem
Maintaining high service performance and user experience in complex web services is challenging due to difficulties in monitoring and logging data across disparate systems, and simulating network metric collection message traffic is complex, especially for third parties building extensions on top of monitoring platforms, with extra resource costs and imprecise testing.
Innovation Solution
State-based observability traffic simulations are generated using a customized simulation configuration expression to define resource types and metrics, allowing for the production of simulated observability metrics through a simulation with simulated resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dummy infrastructure is deployed for observation to demonstrate platform capabilities, then testing and demonstration can be performed, but extra resource costs are incurred and the actual data sent to the platform is not deterministic
Solution Approach 1:
The patent creates a virtual copy of the infrastructure that mimics the behavior and metrics of real infrastructure without requiring physical deployment. The simulation engine generates synthetic observability data that replicates what would be collected from actual resources, eliminating the need for dummy infrastructure while providing deterministic test data.
Solution Approach 2:
The patent replaces the mechanical deployment of physical dummy infrastructure with a software-based simulation system. Instead of physically deploying test resources that generate random data, the system uses a simulation engine that computationally generates deterministic observability metrics based on configured parameters and state transitions.
2Measurement precision
If dummy infrastructure is deployed for observation to perform testing, then demonstration capabilities are enabled, but the testing cannot be precisely controlled
Solution Approach 1:
The patent enables precise control of testing by allowing users to configure specific parameters in the simulation configuration expression, including metric types, resource states, transition conditions, and expected outcomes. The simulation engine interprets these parameters to generate controlled test scenarios with deterministic results, replacing the uncontrolled nature of dummy infrastructure.
Solution Approach 2:
The patent segments the testing control into modular components: configuration expressions define resource types and metrics, state diagrams specify transition logic, and validation rules check expected outcomes. This segmentation allows precise control over each aspect of the simulation without requiring complex integrated infrastructure setup.
3Adaptability or versatility
If third parties build extensions on top of monitoring platforms, then platform functionality is extended, but deeply involved code-based setup and deployment is required with challenging debugging
Solution Approach 1:
The patent introduces a simulation configuration expression as an intermediary layer between the user and the complex platform setup. This configuration language provides a simplified interface that translates high-level user intent into detailed simulation parameters, automatically generating the code-based setup that would otherwise be required. The system handles deployment complexity internally while users work with intuitive configurations.
Solution Approach 2:
The patent creates a universal simulation framework that can model various infrastructure types, metric collections, and observation scenarios through a single configuration expression syntax. This universal approach allows third parties to extend platform functionality without learning multiple specialized setup procedures, as the same configuration mechanism handles diverse simulation needs.
Data Source
AI summary
In one embodiment, a method includes generating a plurality of simulated resources based on a customized simulation configuration expression that defines one or more resource types and a respective number of each of the one or more resource types to generate. The method further includes determining, from the customized simulation configuration expression, observability metrics to be produced in relation to the plurality of simulated resources and a plurality of possible values for the observability metrics and executing a simulation with the plurality of simulated resources to produce simulated observability metrics for the simulation.