Cloud Simulation Resource Configuration via Probability Distribution
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Solution Overview
Problem
Conventional cloud computing-based simulations face challenges in efficiently configuring computing resources, leading to decreased simulation speed and increased risk of failure due to inadequate resource allocation, which results in wasted time and costs.
Innovation Solution
A method and apparatus that dynamically configure optimal cloud computing resources by profiling simulations, calculating resource configuration probability distributions based on historical data, replicating simulations across multiple environments, and executing them simultaneously to identify the most efficient resource configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users arbitrarily select computing resource configuration for simulation, then configuration flexibility is improved, but simulation execution speed deteriorates
Solution Approach 1:
The system performs preliminary actions by profiling simulations in advance, calculating resource configuration probability distributions based on historical data, and pre-selecting optimal resource configurations before actual simulation execution. This allows the system to have optimal configurations ready when simulations are submitted, resolving the contradiction between configuration flexibility and execution speed.
Solution Approach 2:
The system changes parameters by dynamically adjusting resource configuration parameters (CPU, memory, storage, network) based on calculated probability distributions. Instead of fixed or arbitrary configurations, the system selects specific parameter values that maximize simulation execution speed while maintaining flexibility through the probability-based selection mechanism.
2Adaptability or versatility
If users arbitrarily select computing resource configuration for simulation, then configuration flexibility is improved, but simulation reliability deteriorates
Solution Approach 1:
The system implements feedback by continuously monitoring simulation execution results and updating the history database with actual performance data. This feedback loop allows the system to refine resource configuration probability distributions over time, improving reliability while maintaining flexibility through data-driven adjustments rather than arbitrary selections.
Solution Approach 2:
By calculating resource configuration probability distributions in advance based on historical data, the system performs preliminary analysis to identify reliable configurations before actual simulation execution. This preliminary action ensures that only configurations with proven reliability are selected, preventing errors that would occur with arbitrary configurations.
3Manufacturing precision
If simulation execution time is increased to ensure proper resource configuration, then configuration accuracy is improved, but time consumption deteriorates
Solution Approach 1:
The system performs configuration analysis in advance by building probability distributions from historical data before actual simulation execution. This preliminary action eliminates the need for time-consuming trial-and-error configuration adjustments during simulation runtime, achieving both high configuration accuracy and fast execution.
Solution Approach 2:
The system uses historical simulation data as a copy of past experiences to inform current configuration decisions. By analyzing patterns in historical data through probability distributions, the system replicates successful configuration strategies without needing to re-discover them through time-consuming trial executions.
4Productivity
If optimal resource configuration is pursued for each simulation, then simulation execution speed is improved, but system complexity deteriorates
Solution Approach 1:
The system manages complexity by focusing on changing key resource parameters (CPU, memory, storage, network) according to calculated probability distributions. Rather than managing all possible configuration aspects, the system identifies and optimizes the most impactful parameters, achieving high execution speed while keeping the optimization mechanism relatively simple and manageable.
Data Source
AI summary
Disclosed herein are a cloud computing-based simulation apparatus and a method for operating the simulation apparatus. The method for operating a cloud computing-based simulation apparatus includes profiling a simulation in consideration of a simulation model and setup items that are requested by a user, when there is a history corresponding to results of the profiled simulation, calculating a resource configuration probability distribution for the simulation using the history, replicating the simulation environment to multiple simulation environments by selecting resource configurations in N simulation environments depending on the resource configuration probability distribution, where N is an integer of 2 or more, and simultaneously executing simulations in the multiple simulation environments.