Auto-scaling Cloud Clusters Using Multi-Model Confidence Weights
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Solution Overview
Problem
Conventional cloud management systems face challenges in accurately and efficiently provisioning computing resources for large, complex cloud-based computing systems, often leading to inaccurate auto-scaling actions, inefficient resource utilization, and outages due to their inability to handle varying factors and multiple cloud service providers effectively.
Innovation Solution
A digital dynamic auto-scaling system that determines an auto-scaling action by weighing multiple proposed actions from multiple scaling models based on confidence scores, allowing for accurate and efficient allocation of computing resources across complex cloud-based computing systems and multiple cloud service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cloud management systems use single auto-scaling model, then system complexity is low, but auto-scaling accuracy and reliability deteriorate
Solution Approach 1:
The system segments the auto-scaling decision-making process into multiple independent scaling models, each specializing in different aspects (e.g., predictive scaling, reactive scaling, cost optimization). Each model processes specific parameters and provides weighted recommendations, improving overall decision accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system merges multiple scaling models into a unified auto-scaling framework that combines their recommendations through a weighted scoring mechanism. This integration allows the system to leverage the strengths of different models (predictive, reactive, cost-based) to make more reliable scaling decisions than any single model could achieve alone.
2Measurement precision
If conventional cloud management systems use multiple scaling models, then auto-scaling accuracy improves, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where each scaling model's recommendations are evaluated against actual scaling outcomes. The weighted scores and confidence levels are adjusted based on historical performance data, allowing the system to continuously improve measurement precision while automating the complexity management through learned patterns rather than manual configuration.
Solution Approach 2:
The system dynamically adjusts parameters such as model weights, confidence thresholds, and scoring criteria based on system state and historical performance. This allows the multi-model system to adapt its complexity level and decision-making parameters to match current operational requirements, improving accuracy without permanent complexity increases.
3Productivity
If conventional cloud management systems increase computing resources, then processing capability improves, but resource utilization efficiency deteriorates
Solution Approach 1:
The system performs preliminary scaling actions based on predictive models that analyze trends and forecast future resource requirements. By proactively scaling resources before actual demand peaks occur, the system ensures processing capability is available when needed while avoiding the energy waste of over-provisioning resources that remain underutilized.
Solution Approach 2:
The system implements dynamic resource allocation where computing resources are continuously adjusted based on real-time demand signals and model predictions. This dynamic approach allows the system to maintain high processing capability during peak periods while reducing resource allocation during low-utilization periods, optimizing the balance between productivity and energy efficiency.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media are disclosed for determining an accurate and efficient auto-scaling action for a cloud-based computing cluster based on multiple proposed auto-scaling actions from multiple scaling models. For example, the disclosed systems can determine an auto-scaling action to perform on a cloud-based computing cluster by weighing multiple proposed auto-scaling actions from multiple scaling models based on confidence scores associated with the proposed auto-scaling actions. Moreover, the disclosed systems can modify the cloud-based computing cluster using the determined auto-scaling action (e.g., to accurately and efficiently provision computing resources for a cloud-based computing system).