Service Virtualization for Stable Training Environments
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Solution Overview
Problem
Training new users on a live data management system is challenging due to frequent updates, server re-boots, and service outages, which make it difficult to provide a stable and consistent training environment.
Innovation Solution
A system and method using a host server for service virtualization, which retrieves live data, converts it into virtualized data by deidentifying personal information and simplifying complex fields, and processes it using virtualized resources to generate a stable training environment, allowing developers to test and train without accessing the actual live components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training is conducted on a live data management system, then users gain access to real data and actual system functions, but the training environment becomes unstable due to frequent updates, server re-boots, and service outages
Solution Approach 1:
The patent creates a virtualized copy of the live enterprise service that replicates its data structures, functions, and behavior patterns. This virtual instance provides a stable training environment that mirrors the real system without being subject to its instability, allowing trainees to practice with realistic data while the live system remains unaffected
Solution Approach 2:
The virtualized service acts as an intermediary layer between trainees and the live enterprise service. It intercepts training requests, processes them against virtualized data, and returns simulated responses, thereby preventing direct interaction with the live system while maintaining training realism
2Loss of information
If live data is used for training, then training materials reflect current system state, but personal information and complex fields must be managed
Solution Approach 1:
The virtualization process applies different transformation rules to different data fields based on their sensitivity and complexity. Personal identifiable information is masked or syntheticized, while training-relevant fields maintain their original structure and relationships, creating a selectively modified data set that protects privacy while preserving training value
3Reliability
If developers wait for enterprise service enhancements before training, then training reflects latest system capabilities, but development productivity decreases
Solution Approach 1:
The virtualized training environment can be prepared and populated with representative data in advance, before the live system is fully updated or available. This allows development and training activities to proceed in parallel, with the virtual environment serving as a preview or placeholder that doesn't require waiting for enterprise service enhancements
Data Source
AI summary
Disclosed herein are processor-executable methods, computing systems, and related technologies for using a host server and service virtualization to mimic functions of a live enterprise service in order to provide a stable training environment for users of a data management system. Embodiments may include retrieving live data from the enterprise service. The live data may then be converted to virtualized data by deidentifying personal information, simplifying complex fields, and adjusting date fields, such that the virtualized data is an accurate representation of the live data formatting and is optimized for training purposes. The virtualized data may be processed using one or more virtualized resources on the host server, wherein the virtualized resources may correspond to functions of the enterprise service. A virtualized response may be generated in response to a user request to the host server.