SaaS Performance Prediction via ML Model Adaptation
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Solution Overview
Problem
Current technologies lack effective means to measure and manage performance factors affecting response times in SaaS systems, leading to inefficient resource allocation and increased costs due to diminishing returns from adding additional computing resources.
Innovation Solution
A data processing system that programmatically receives performance metrics, determines data throughput and relative capacity, adapts machine learning prediction models, and transmits threshold values to optimize performance factors, using models like Amdahl, Gustafson, and universal scalability to predict and adjust system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If additional computing resources are added to decrease response times, then response times improve, but costs increase due to diminishing returns
Solution Approach 1:
The system performs preliminary prediction of system state using machine learning models before actual performance degradation occurs. By predicting future performance based on current performance factors, the system can proactively adjust configuration values to maintain optimal response times without continuously adding computing resources.
Solution Approach 2:
The system changes configuration parameters (performance factors) of existing computing resources to optimize performance. Instead of adding more resources, the system adjusts configuration values such as approval chain conditions, cache sizes, and thread pool sizes to achieve better response times with the same resource quantity.
2Speed
If configuration values are adjusted to optimize performance, then response times improve, but system complexity increases
Solution Approach 1:
The system implements continuous feedback loops where performance metrics are monitored, predicted using machine learning models, and used to automatically adjust configuration values. This closed-loop control system simplifies management by replacing manual configuration tuning with automated, data-driven adjustments based on actual system performance.
Solution Approach 2:
The system performs self-optimization by automatically adjusting its own configuration values based on predicted performance outcomes. The machine learning models enable the system to autonomously determine optimal configuration settings without requiring external intervention or complex manual tuning procedures.
3Productivity
If performance monitoring and prediction systems are implemented, then resource allocation efficiency improves, but device complexity increases
Solution Approach 1:
The machine learning prediction models serve multiple functions: they predict system state, identify performance bottlenecks, recommend configuration changes, and validate the impact of adjustments. This multi-functional approach consolidates what could be multiple separate monitoring and optimization tools into a unified system, reducing overall complexity while maintaining high resource allocation efficiency.
Data Source
AI summary
In one or more embodiments, a system and/or a method may implement: receiving data representing different performance behavior metrics that are associated with software instances that are respectively associated with consumer computers and that specify values of performance factors of the software instances as the consumer computers interoperate with the software instances; determining data throughput values that represent processing throughput of the software instances; determining relative capacity values of the software instances; adapting each of prediction models to the relative capacity values; determining correlation coefficients from the prediction models and the multiple data throughput values; executing a prediction model of the prediction models associated with a correlation coefficient closest to a value of “1” to result in outputting threshold values associated with the performance factors; and transmitting the threshold values to a particular consumer computer among the consumer computers as part of a change recommendation message having a hyperlink.