ML-Based Cloud Resource Allocation With Risk-Scored Server Changes
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Solution Overview
Problem
Managing complex cloud server environments is challenging due to difficulty in optimizing server configurations, particularly in view of application constraints and changing risk tolerances, making it difficult for administrators to assess and implement changes effectively.
Innovation Solution
A machine learning model is trained on server environment performance measurements to identify recommended changes, providing risk scores and natural language descriptions of adjustments, which can be automatically implemented or presented to users for approval, considering application constraints and historical data to optimize server environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If administrators manually manage and optimize complex cloud server environments, then they can assess and control risks associated with changes, but the sheer volume, complexity, and nuance of server environments make it difficult to manage and modify them effectively
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between administrators and complex cloud server environments. This system automatically analyzes server configurations, identifies optimization opportunities, and assesses risks associated with potential changes, thereby simplifying the management interface without reducing the underlying system complexity
Solution Approach 2:
The system enables self-service automation where the cloud management system autonomously performs analysis, recommendation generation, and risk assessment functions. This reduces the manual burden on administrators while maintaining control over complex server environments through automated intelligence
2Productivity
If administrators switch application servers between different cloud environments to optimize performance, then speed and availability may improve, but it becomes difficult to assess the risk of such changes
Solution Approach 1:
The system performs preliminary risk assessment and analysis before administrators implement cloud environment switching decisions. By pre-evaluating potential risks and outcomes of proposed changes, administrators can make informed decisions that balance performance optimization with risk management
Solution Approach 2:
The system provides feedback mechanisms that continuously monitor server performance and risk metrics. This feedback loop enables administrators to assess the actual impact of cloud environment switching decisions and adjust future decisions based on observed outcomes, thereby improving both performance and risk management over time
3Measurement precision
If the system provides detailed recommendations for server configuration changes, then optimization opportunities can be identified, but the recommendations must be presented in a way that is understandable to users
Solution Approach 1:
The patent segments complex optimization recommendations into distinct, manageable components. Each recommendation is broken down into specific actionable items with clear descriptions of expected benefits, risks, and implementation steps, making detailed technical information accessible to users with varying levels of expertise
Data Source
AI summary
Methods, systems, and apparatuses are described herein for automatically recommending cloud server configuration changes. A machine learning model may be trained to output infrastructure modification recommendations based on a history of performance measurements of a server environment that executes one or more applications. Input data indicating a current configuration of the server environment may be provided to the trained machine learning model. In response, the trained machine learning model may output a recommended change to the server environment. Based on comparing the risk score to a threshold associated with the one or more applications, users might be provided with the option to implement the recommended change by modifying one or more operating parameters of one or more servers of the server environment. Additionally and/or alternatively, the recommended change may be automatically implemented.