Automated Cloud Infrastructure Optimization via Policy-Driven Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud infrastructure management faces challenges in maintaining desired states of cost, security, performance, and availability due to increased complexity and rapid change, requiring active management and automated policy-driven approaches to optimize cloud environments.
Innovation Solution
A computer-implemented, automated cloud infrastructure optimization system that uses a monitoring system to gather data, evaluates policies, and produces recommendations for changes to achieve desired states, while obtaining necessary credentials through approval workflows and security systems to execute these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and tuning of cloud infrastructure is performed, then control over desired state is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system enables automated self-service through policy-driven monitoring and tuning. The monitoring system continuously tracks infrastructure state against defined policies, and the tuning system automatically executes corrective actions without human intervention, allowing the system to maintain its own desired state
Solution Approach 2:
The system implements continuous feedback loops where the monitoring system detects deviations from desired state, triggers policy evaluation, and activates tuning actions. This closed-loop feedback mechanism ensures automatic correction of infrastructure drift while minimizing manual involvement
2Loss of time
If automated monitoring and tuning systems are implemented, then time and operational effort are reduced, but system complexity increases
Solution Approach 1:
The system segments automation into distinct modular components: monitoring system for data collection, policy engine for decision logic, and tuning system for execution. This segmentation reduces overall complexity by allowing each component to be developed, maintained, and scaled independently
Solution Approach 2:
The policy engine serves as an intermediary layer between monitoring and tuning functions. It abstracts the complex decision-making logic into manageable policies, simplifying the interaction between monitoring data and tuning actions while maintaining system coherence
3Productivity
If frequent tuning actions are executed to maintain desired state, then infrastructure optimization is improved, but risk of errors and security vulnerabilities increases
Solution Approach 1:
The system performs preliminary validation by evaluating proposed tuning actions against predefined policies before execution. This pre-check mechanism ensures that only authorized and validated changes are applied, reducing the risk of errors while maintaining optimization effectiveness
Solution Approach 2:
The policy engine implements preliminary anti-action by defining constraints and approval workflows that prevent potentially harmful tuning actions. This proactive protection mechanism blocks risky operations before they can compromise system stability
Data Source
AI summary
A system and method for optimizing a cloud environment using a workflow.