Digital Twin Ecosystems for Automated Infrastructure Tuning
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Solution Overview
Problem
In complex enterprise data management ecosystems, diagnosing issues and predicting behaviors is challenging due to the vast number of permutations and the inability to model dynamic configurations, leading to undiagnosed bugs and defects that cause business disruptions and compliance issues, with no mechanism to safely simulate enterprise environments for vendors to test their products.
Innovation Solution
The creation of digital twin ecosystems using data confidence fabric techniques, which track data management operations in a ledger and enable real-time predictive outcomes, allowing vendors to simulate and assess changes before deployment, and provide a 'snapshot' of system state for root-cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vendors deploy automated infrastructure tuning software in complex enterprise ecosystems, then the software can perform automated tuning operations, but the software cannot predict or diagnose issues due to the vast number of permutations and dynamic configurations
Solution Approach 1:
The patent applies preliminary action by creating digital twin ecosystems and simulation environments before actual software deployment. Vendors can qualify and test their automated infrastructure tuning software in virtualized replicas of enterprise ecosystems, identifying potential issues before real-world deployment. This allows the software to be pre-tested across numerous permutations and configurations without risking production systems.
Solution Approach 2:
The patent implements copying by creating digital twins - virtual copies of enterprise data management ecosystems. These digital twins replicate the complex snowflake configurations, hardware, and software environments, allowing vendors to test and diagnose issues in the copy rather than the original system. This copying approach enables reliable testing of automated tuning software across diverse enterprise environments without affecting actual operations.
2Adaptability or versatility
If enterprise ecosystems use diverse snowflake configurations of hardware and software, then the systems can be customized to specific needs, but it becomes impossible to qualify products for all possible permutations
Solution Approach 1:
The patent applies universality by creating a universal digital twin ecosystem framework that can replicate diverse enterprise configurations. The simulation environment is designed to accommodate various snowflake configurations of hardware, software, and data management ecosystems through standardized virtualization interfaces. This allows a single product qualification process to test across multiple permutations universally, rather than requiring separate qualification for each configuration.
Solution Approach 2:
The patent uses copying to create virtual replicas of diverse enterprise ecosystems. Instead of physically testing products in every possible snowflake configuration, the system creates digital copies of these environments with varying hardware, software, and configuration permutations. This enables comprehensive product qualification across adaptable configurations without the exponential complexity of physical testing.
3Productivity
If bugs or defects penetrate complex enterprise environments, then the systems can handle diverse workloads, but the defects cause havoc with little trace of what went wrong
Solution Approach 1:
The patent applies preliminary action by implementing monitoring and logging mechanisms in the digital twin environments before defects occur. The simulation systems track data management operations, configuration changes, and system states in advance, creating a historical record that can be analyzed when issues arise. This preliminary tracking enables easier defect detection and root cause analysis compared to unmonitored production environments.
Solution Approach 2:
The patent uses the digital twin as an intermediary for defect analysis. When bugs or defects occur in the virtualized environment, the digital twin serves as a mediator that preserves the system state and operational context. This intermediary preserves traces of what went wrong, including configuration states and operational data, making it possible to diagnose defects and perform root cause analysis without needing direct access to the complex production environment.
4Productivity
If vendors cannot simulate enterprise environments for testing, then development is faster, but products cannot be properly qualified before deployment
Solution Approach 1:
The patent implements copying by creating virtualized digital twin environments that replicate enterprise ecosystems. Vendors can test and qualify their software products in these copied environments, achieving proper quality assurance without the need for physical enterprise deployments. The digital copies provide realistic testing conditions while maintaining development speed, as the virtual environments can be rapidly provisioned and configured.
Solution Approach 2:
The patent uses digital twin simulation environments as intermediaries between development and production. The simulation platform serves as a mediator that provides a controlled testing ground between rapid development cycles and production deployment. This intermediary enables proper product qualification through comprehensive testing while maintaining the speed of modern software development methodologies.
Data Source
AI summary
One example method includes receiving a transaction at a digital twin that incorporates all transactions that have occurred at a site from which the transaction was received, and wherein the digital twin was created based in part on a data confidence fabric ledger, entering the transaction in the data confidence fabric ledger at the digital twin, receiving another transaction at the digital twin, wherein the another transaction has caused a problem to occur, entering the another transaction in the data confidence fabric ledger, replaying any transactions that have occurred in a defined time window that includes the another transaction, based on the replaying, identifying a state of a system where the problem occurred, and a time when the problem occurred, and determining a resolution to the problem.