Monte Carlo Quantile Sampling for Faster Microservice Simulation
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Solution Overview
Problem
Existing network-based microservices face inefficiencies in performing discrete Monte Carlo simulations due to the high volume of data required for quantile samples, leading to slow response times and increased network demands.
Innovation Solution
A method involving a pre-calculation server that reduces the size of quantile samples from 1/M probability steps to 1/N steps, with additional samples added for unbounded variables, followed by linear interpolation to restore the data set, enabling efficient Monte Carlo simulations with reduced data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard set of quantile samples with 1/M probability steps is used for Monte Carlo simulation, then simulation accuracy is maintained, but network demands and response time increase significantly
Solution Approach 1:
The patent extracts only the essential quantile samples needed for accurate simulation by reducing the sample set from 1/M probability steps to 1/N probability steps (where N < M). This extraction principle removes redundant data points while preserving the critical information required for Monte Carlo simulation accuracy, thereby reducing network transfer time and improving response time without sacrificing simulation precision.
Solution Approach 2:
The patent applies partial action by transmitting a reduced set of quantile samples (1/N steps) instead of the complete standard set (1/M steps). The reduced sample set is sufficient for achieving accurate simulation results, making the transmission of all samples excessive. This partial approach maintains simulation accuracy while significantly reducing network demands and response time.
2Measurement precision
If a standard set of quantile samples with 1/M probability steps is used for Monte Carlo simulation, then simulation accuracy is maintained, but network demands increase
Solution Approach 1:
The patent extracts only the necessary quantile samples by reducing the probability steps from 1/M to 1/N, removing redundant data points. This extraction reduces the volume of data that needs to be stored and transmitted over the network, directly addressing the quantity of substance parameter while preserving simulation accuracy through selective sampling of critical quantile values.
Solution Approach 2:
The patent transmits a partial set of quantile samples (1/N steps) instead of the complete standard set (1/M steps). This partial transmission provides sufficient data for accurate Monte Carlo simulation without the excess of transmitting all possible quantile samples, thereby reducing network data volume and storage requirements while maintaining measurement precision.
3Productivity
If quantile samples are reduced from 1/M probability steps to 1/N steps, then network demands and response time decrease, but simulation accuracy may be compromised
Solution Approach 1:
The patent changes the probability step parameter from 1/M to 1/N (where N < M) to reduce data volume and improve simulation efficiency. This parameter change is strategically selected to maintain simulation accuracy by preserving sufficient quantile sample density. The optimization involves finding the optimal N value that balances reduced data transmission with maintained simulation precision, thereby improving productivity without compromising measurement precision.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving from a database server via a network, a reduced size set of quantile samples of a plurality of variables associated with a dynamic system, wherein the reduced size set of quantile samples is based on a standard set of quantile samples, wherein the standard set of quantile samples comprises M quantile samples based on dividing each distribution of the plurality of variables into 1/M probability steps, and wherein the reduced size set of quantile samples comprises N quantile samples based on dividing each distribution of the plurality of variables into 1/N probability steps, performing a plurality of linear interpolations upon the reduced size set of quantile samples to a restored size set of quantile samples, performing a Monte Carlo simulation using the restored size set of quantile samples to generate a simulation result, and transmitting the simulation result to a client device. Other embodiments are disclosed.