Self-Healing VM Cluster Management via Adaptive Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing complexity of multi-cloud software-defined networks (SDN) infrastructure requires a technique for self-healing and dynamic optimization of virtual machine (VM) server cluster management to ensure high and stable performance of cloud services, as traditional methods struggle with performance monitoring and automation in network function virtualization (NFV) environments.
Innovation Solution
A self-healing and dynamic optimization (SHDO) methodology is developed, utilizing adaptive thresholding to autonomously trigger optimization in VM server clusters, which includes real-time analytics for root cause determination, self-healing policy management, and dynamic performance tuning across various metrics like CPU, memory, and network interface card usage, enabling closed-loop automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional management methods are used in NFV environments, then implementation is simpler, but performance monitoring and automation capabilities are insufficient
Solution Approach 1:
The patent implements self-healing and dynamic optimization capabilities that enable the VM server cluster to automatically monitor its own performance, detect anomalies, and execute corrective actions without external intervention. The system autonomously collects performance metrics, compares them against thresholds, and triggers healing operations when degradation is detected, making the management system serve itself.
Solution Approach 2:
The patent establishes closed-loop feedback mechanisms where performance metrics are continuously collected from the VM server cluster, analyzed against predefined thresholds, and used to trigger automated responses. The system monitors quality metrics, detects when they fall outside adaptive thresholds, and feeds this information back to execute self-healing operations, creating a continuous monitoring-response cycle.
2Adaptability or versatility
If static threshold monitoring is used, then implementation is simpler, but it cannot adapt to dynamic cloud environments
Solution Approach 1:
The patent transitions from static threshold values to dynamic adaptive thresholds that automatically adjust based on historical performance data and changing environmental conditions. The system calculates adaptive thresholds using statistical methods on accumulated performance metrics, allowing the monitoring system to adapt to normal variations in cloud environment performance while maintaining sensitivity to actual anomalies.
Solution Approach 2:
The patent performs preliminary actions by accumulating historical performance data and establishing baseline behavior patterns before anomalies occur. The system continuously collects and stores performance metrics, using this historical information to pre-calculate adaptive thresholds and prepare for future anomaly detection, rather than reacting only when problems arise.
3Productivity
If manual intervention is used for performance optimization, then system complexity is lower, but productivity and response time are reduced
Solution Approach 1:
The patent implements self-healing operations that automatically execute performance optimization tasks without human intervention. When anomalies are detected, the system autonomously initiates healing operations such as restarting services, reallocating resources, or adjusting configuration parameters, thereby maintaining high productivity while minimizing the need for manual operational intervention.
Solution Approach 2:
The patent ensures continuous performance monitoring and optimization by maintaining uninterrupted collection and analysis of performance metrics. The system operates continuously to detect anomalies and execute healing operations, eliminating gaps in monitoring that would occur with manual intervention, thereby maintaining constant productivity and rapid response to performance degradation.
Data Source
AI summary
Virtual machine server clusters are managed using self-healing and dynamic optimization to achieve closed-loop automation. The technique uses adaptive thresholding to develop actionable quality metrics for benchmarking and anomaly detection. Real-time analytics are used to determine the root cause of KPI violations and to locate impact areas. Self-healing and dynamic optimization rules are able to automatically correct common issues via no-touch automation in which finger-pointing between operations staff is prevalent, resulting in consolidation, flexibility and reduced deployment time.