Cloud Infrastructure Parameter Tuning Using ML Cluster Models
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Solution Overview
Problem
Large-scale cloud infrastructures face challenges in efficient and cost-effective tuning due to the complexity and scale of parameters, with manual tuning being time-consuming and error-prone, and conventional ML approaches being impractical for exabyte-scale environments.
Innovation Solution
A data-driven tuning service that leverages descriptive and predictive ML models to automate parameter tuning, using observational, hypothetical, and experimental tuning methods, focusing on cluster dynamics and operational parameters like YARN configurations and hardware design, without requiring extensive experimentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning is used to optimize cloud infrastructure parameters, then tuning precision can be achieved, but time consumption and error rate increase significantly
Solution Approach 1:
The patent replaces manual mechanical tuning processes with automated machine learning systems. The ML models automatically analyze cloud infrastructure parameters, predict optimal configurations, and apply tuning recommendations without human intervention, thereby maintaining precision while eliminating time consumption and human error.
Solution Approach 2:
The cloud infrastructure system performs self-tuning through automated ML-driven processes. The system monitors its own performance metrics, identifies optimization opportunities, and automatically adjusts parameters without requiring external manual intervention, achieving both precision and time efficiency.
2Extent of automation
If conventional ML approaches are used for parameter tuning, then automation is achieved, but scalability to large-scale infrastructures is lost
Solution Approach 1:
The patent segments the large-scale cloud infrastructure into manageable components and applies ML models at appropriate levels (individual services, clusters, or entire infrastructure). This segmentation enables the automated tuning system to scale effectively by processing smaller units independently while maintaining overall system optimization.
Solution Approach 2:
The patent extends ML approaches to handle multi-dimensional aspects of large-scale infrastructure by incorporating hierarchical modeling that operates across different levels of abstraction. This allows the system to maintain automation while adapting to the complexity and scale of enterprise cloud environments.
3Manufacturing precision
If experimental tuning is performed on large-scale cloud infrastructures, then parameter optimization can be achieved, but operational stability and business continuity are compromised
Solution Approach 1:
The patent uses ML models to predict optimal parameter configurations before applying them to production systems. By pre-evaluating tuning recommendations through simulation and prediction on historical data, the system identifies safe, optimized configurations that maintain operational stability while improving performance.
Solution Approach 2:
The system continuously monitors cloud infrastructure performance and uses this feedback to refine ML model predictions. This closed-loop approach ensures that parameter optimizations are based on actual system behavior and performance outcomes, maintaining reliability while achieving optimization goals.
Data Source
AI summary
An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.). Rich “observational” models (models collected without modifying the system) are combined with judicious use of “fighting” (testing in production), allowing the tuning service to automatically configure operational parameters of a large cloud infrastructure for a broad range of applications.