Automated Cluster Configuration via Multi-Layer Machine Learning
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Solution Overview
Problem
Datacenter operators face difficulties in determining and maintaining optimal configuration parameters for clusters of computing devices across large-scale infrastructure environments, with hundreds to thousands of configuration options, making manual configuration impractical and inefficient, especially when clusters belong to different entities with varying objectives such as performance, cost, and security.
Innovation Solution
An automated system using machine learning techniques identifies multiple layers of clusters and provides objective functions to recommend configuration options, narrowing down the parameter space through clustering, feature selection, and dual-step analysis, enabling near real-time monitoring and prediction of configuration changes' impacts on cluster performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration methods are used by datacenter operators, then configuration decisions can be made with expert knowledge, but the process becomes inefficient and impractical when expanded to large numbers of computing devices
Solution Approach 1:
The system enables computing devices to automatically self-configure by collecting their own operational data, performing self-diagnosis, and adjusting configuration parameters without human intervention. The autonomous learning model processes device telemetry and automatically generates configuration recommendations, allowing the system to serve itself rather than requiring operator expertise for each device.
Solution Approach 2:
The patent replaces the manual mechanical process of operator configuration with an automated computational system. Machine learning algorithms and autonomous learning models substitute for human operators, automatically analyzing device performance data and generating configuration changes based on learned patterns rather than human expertise.
2Reliability
If configuration parameters are optimized for specific objectives such as performance or security, then device performance improves, but the complexity of deciding which optimizations to implement increases significantly
Solution Approach 1:
The system dynamically changes configuration parameters based on learned patterns from device telemetry data. The autonomous learning model automatically adjusts multiple configuration parameters simultaneously (CPU settings, memory allocation, software installations, security policies) based on real-time performance feedback, eliminating the need for manual optimization decisions.
Solution Approach 2:
The patent segments the configuration optimization problem into multiple independent layers: hardware configuration, software configuration, security configuration, and performance tuning. Each layer can be independently optimized by specialized machine learning models, allowing complex configuration tasks to be broken down into manageable segments that are automatically processed.
3Reliability
If configuration parameters are continuously updated to maintain optimal performance, then device performance is maintained, but the difficulty of maintaining consistency across all computing devices increases
Solution Approach 1:
The system implements continuous feedback loops where device telemetry data is collected, analyzed by the autonomous learning model, and used to generate configuration recommendations. The effects of configuration changes are monitored and fed back into the learning model, creating a closed-loop system that automatically maintains optimal performance across all devices through continuous adaptation rather than manual management.
Data Source
AI summary
Systems and methods are provided for computationally configuring computing devices and performing multi-layer cluster analysis. For example, the system can identify multiple layers of clusters of devices (e.g., shared hardware configuration, shared application configuration, number of applications, etc.) in a large scale infrastructure environment automatically. For each layer of the clusters of devices, parameters of these devices are provided to a machine learning model to produce an objective function (e.g., minimum number of devices, utilization under 80%, etc.), whose output can be provided to a datacenter operator or other user in the large scale infrastructure environment so they can make further configuration changes to the devices in each cluster.