Cloud-Based POS Testing With Simulated Worker Swarm
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Solution Overview
Problem
Existing payment processing systems face challenges in determining the maximum number of clients that can connect simultaneously and process transactions in parallel, as conventional testing methods fail to adequately simulate the stress of multiple client interactions, particularly due to differing protocols and languages used by client devices.
Innovation Solution
The implementation of a cloud-based testing system that uses multiple autonomous simulated clients, known as 'simulated workers,' to generate and execute synthetic transactions, mimicking the behavior of real clients and stressing the payment processing network to determine its limitations and strengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional testing methods are used to submit individual synthetic transactions, then the testing process is simple to implement, but the simulation of stress from multiple client interactions is inadequate
Solution Approach 1:
The patent creates simulated worker instances that copy the behavior patterns, protocols, and interaction methods of real client devices. These simulated workers replicate authentication sequences, transaction submission patterns, and communication protocols to accurately stress-test the POS system without requiring actual multiple physical clients.
Solution Approach 2:
The testing system introduces an intermediary layer of simulated workers that mediate between the test controller and the POS system. These simulated workers translate test commands into realistic client-device interactions, enabling accurate stress simulation while maintaining controlled testing conditions.
2Productivity
If multiple simulated workers are deployed to simulate high transaction volumes, then the system can accurately determine parallel processing capacity, but the complexity of configuring and managing simulated workers increases
Solution Approach 1:
The testing system segments the simulated worker population into multiple independent instances, each capable of autonomous operation. This segmentation allows the system to test parallel processing capacity by distributing transaction loads across multiple simulated workers while maintaining individual worker simplicity and manageability.
Solution Approach 2:
The system dynamically configures and scales the number of simulated workers based on testing requirements. Workers can be created, activated, and terminated on-demand, allowing flexible adjustment of parallel processing simulation without permanent system complexity increases.
3Reliability
If simulated workers use differing protocols and languages to mimic real client devices, then the testing realism improves, but the difficulty of detecting and measuring system performance increases
Solution Approach 1:
The system employs intermediary components that translate diverse protocols and communication languages used by different simulated workers into a standardized measurement format. This intermediary layer maintains testing realism by supporting multiple protocols while simplifying performance detection and measurement through uniform data collection.
4Reliability
If a large number of clients are simulated simultaneously, then the system can identify bottlenecks and optimize infrastructure, but the computational resources required for testing increase
Solution Approach 1:
The testing system implements staged testing approaches where simulated workers are activated in progressive batches rather than all simultaneously. This allows identification of bottlenecks through incremental load increases while controlling computational resource consumption by testing with partial worker populations first and escalating only as needed.
Data Source
AI summary
A computer-implemented method for cloud-based testing of a payment network may include receiving a test configuration for testing a payment processing network, configuring a simulated worker generator for generating a plurality of simulated workers according to the received test configuration, reading commands to be executed by each simulated worker among the plurality of simulated workers from a command bank according to the received test configuration, configuring the plurality of simulated workers according to the commands and the received test configuration, starting a swarm test of the payment processing network by the plurality of simulated workers, reading results of the swarm test from the plurality of simulated workers, and saving the results to storage.