Server Health Prediction Model for Dynamic Load Balancing
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Solution Overview
Problem
Current methods for analyzing server health based on latency are not robust and fail to facilitate effective load balancing decisions or early recognition of impending server saturation, as they rely on static thresholds that may not accurately identify health problems over time.
Innovation Solution
A method involving a health analysis apparatus that monitors network traffic using a server health prediction model, which applies historical signal data to dynamically predict anomalies and initiate mitigation actions, improving load balancing and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static thresholds are used for server health analysis, then the system is simple to implement, but the accuracy of identifying server health problems deteriorates over time
Solution Approach 1:
The patent transforms static thresholds into dynamic thresholds that automatically adapt to changing server conditions. The system continuously learns from historical data and adjusts thresholds over time, enabling accurate identification of health problems without manual reconfiguration while maintaining operational simplicity.
Solution Approach 2:
The system implements self-service through automated threshold adjustment using machine learning algorithms. The server health analysis apparatus automatically learns from historical data and adjusts thresholds without requiring manual intervention from administrators, maintaining both accuracy and ease of implementation.
2Ease of operation
If latency-based monitoring is used, then the implementation is straightforward, but the ability to recognize impending server saturation deteriorates
Solution Approach 1:
The system performs preliminary action by predicting future server saturation before it occurs. Using machine learning models that analyze historical data patterns, the system forecasts potential health issues and triggers early warnings, enabling proactive mitigation before actual saturation happens while maintaining simple monitoring operations.
Solution Approach 2:
The system implements feedback loops where monitoring data is continuously fed back into machine learning models that adjust predictions and thresholds. This feedback mechanism improves the reliability of early saturation detection while keeping the monitoring system operationally simple through automated adaptation.
3Measurement precision
If dynamic prediction models are used, then the accuracy of health anomaly detection is improved, but the system complexity increases
Solution Approach 1:
The system reduces complexity through self-service automation. Machine learning models automatically train on historical data, dynamically adjust thresholds, and generate predictions without requiring complex manual configuration or expert intervention. This maintains high prediction accuracy while keeping the system manageable through automated operations.
Solution Approach 2:
The system manages complexity by dynamically changing parameters such as thresholds and prediction models based on learned patterns from historical data. Rather than requiring complex fixed configurations, the system adapts parameters automatically, improving accuracy while maintaining operational simplicity through parameter self-adjustment.
Data Source
AI summary
A method, non-transitory computer readable medium, and health analysis apparatus that monitors network traffic exchanged with a plurality of server devices in a server pool to obtain signal data regarding a plurality of signals associated with the network traffic. A determination is made when there is a sever health anomaly for one or more of the server devices based on an application of a server health prediction model to the signal data. The server health prediction model includes a plurality of predictive health targets each based at least in part on historical signal data for one or more of the signals and having an associated threshold value. A mitigation action is initiated when the determining indicates there is a sever health anomaly for one or more of the server devices.