Cloud Topology Simulation for Predicting Change Impact
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Solution Overview
Problem
The increasing complexity of cloud networks makes it difficult for administrators to accurately predict the effects of changes to cloud network topologies, leading to unintended negative consequences such as increased costs or reduced uptime.
Innovation Solution
A computing device provides a user interface for editing cloud infrastructure topologies and simulates the effects of modifications using machine learning and probabilistic methods, generating simulation data to identify recommended changes that consider performance, cost, and security, and automatically implements these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud network topologies are designed with increased complexity to improve performance and reliability, then network stability and service quality are improved, but the difficulty of detecting and measuring the effects of changes increases
Solution Approach 1:
The patent creates a virtual copy (simulation environment) of the cloud network topology that mirrors the real network's structure and behavior. This simulation copy allows administrators to observe and measure the effects of topology changes without affecting the actual production network, thereby resolving the difficulty of detecting and measuring change effects in complex cloud networks.
Solution Approach 2:
The simulation system acts as an intermediary between administrators and the complex cloud network. It provides a simplified interface where administrators can input proposed changes and receive predicted effects, mediating the interaction with the complex underlying network infrastructure and making change effects measurable and detectable.
2Adaptability or versatility
If cloud network topologies are manually designed and modified by administrators, then flexibility and adaptability are maintained, but the complexity of managing and optimizing the network increases
Solution Approach 1:
The system enables self-service by automatically generating optimized network topologies and change recommendations based on simulated performance data. Instead of requiring administrators to manually analyze complex network configurations, the system autonomously processes topology proposals, runs simulations, and generates optimization recommendations, thereby reducing management complexity while maintaining design flexibility.
Solution Approach 2:
The simulation system provides feedback to administrators about the predicted effects of proposed topology changes before implementation. This feedback loop includes performance predictions, cost estimates, and reliability assessments, enabling informed decision-making while reducing the cognitive load and complexity of network management through automated analysis.
3Productivity
If cloud service providers are switched between different providers to optimize performance and cost, then service quality and cost efficiency are improved, but the complexity of configuring and migrating elements increases
Solution Approach 1:
The system performs preliminary simulation and analysis before actual cloud service provider migration or reconfiguration occurs. By pre-simulating the effects of switching elements between different cloud providers and pre-configuring optimized topologies, the system reduces the complexity and risk of actual migration operations while achieving cost efficiency through data-driven provider selection.
Data Source
AI summary
Systems, methods, and apparatuses are described for automatically generating and simulating cloud infrastructure topologies. A computing device may provide a user interface displaying a cloud infrastructure topology and may receive user input indicating modifications to that topology. The computing device may generate simulation data by determining estimated resources used by each element of the modified version of the cloud infrastructure topology during simulation of the modified version of the cloud infrastructure topology. Such simulation might be determined by simulating events, probabilistically, and/or through use of a trained machine learning model. The computing device may then identify recommended changes to the modified version of the cloud infrastructure topology based on that simulation data and cause output of those recommended changes. Such recommendations might relate to moving various elements to different cloud service providers to, for example, improve stability and/or lower costs.