Virtual Machine Profile Deviation Detection and Remediation
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Solution Overview
Problem
In virtualized computer environments, deviations in virtual machine behavior can compromise performance, and existing methods lack effective mechanisms for real-time monitoring and remediation, leading to inefficiencies and potential security risks.
Innovation Solution
A system that analyzes static and dynamic data to generate virtual machine profiles, detects deviations, and performs automatic remediation operations, including transmitting alerts, storing data, and making operational changes such as network adjustments or shutting down virtual machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and automatic remediation systems are implemented, then virtual machine performance and security are improved, but system complexity and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by generating virtual machine profiles based on historical data and expected behavior patterns before deviations occur. These profiles serve as pre-established benchmarks that enable automatic detection and remediation when anomalies are detected, eliminating the need for complex real-time analysis of every system state change.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring virtual machine behavior, comparing it against established profiles, and automatically initiating remediation actions when deviations are detected. This closed-loop feedback system maintains reliability through automated correction without requiring complex manual intervention procedures.
2Measurement precision
If comprehensive data analysis is performed on multiple virtual machines, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the analysis by creating distinct virtual machine profiles for different types of virtual machines based on their specific behaviors and characteristics. This segmentation allows for specialized, optimized analysis of each profile type rather than applying generic complex analysis to all virtual machines uniformly, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The system applies partial action by focusing analysis only on specific behavioral parameters and deviation thresholds that are most relevant to detecting actual issues. Rather than analyzing every possible parameter in detail, the system concentrates computational resources on the most critical indicators, achieving effective detection with reduced processing overhead.
Data Source
AI summary
Methods, systems and computer program products for identifying virtual machines that perform inconsistent with a profile are provided. Methods may include collecting initial virtual machine data corresponding to multiple virtual machines. Multiple virtual machine profiles are generated and each of the virtual machine profiles is associated with one of multiple virtual machine types. Ones of the virtual machines are associated with one of the virtual machine profiles based on the virtual machine data. Additional virtual machine data corresponding to ones of the virtual machines is collected. The additional virtual machine data is analyzed to detect a deviation of one of the virtual machines.