Intelligence Agent For Network Service And Resource Management

Abstract

Silent Performance Degradation In Network Systems Is A Critical Operational Challenge Where Servers Gradually Lose Efficiency Without Explicit Failures Or Alarms, Leading To Reduced Reliability, Increased Downtime Risk, And Higher Maintenance Costs. Existing Monitoring Systems Depend On Threshold Alerts And Reactive Logs, Missing Gradual Performance Drift And Producing False Alarms During Spikes. They Also Lack Interpretability And Do Not Offer Clear Root-cause Insights Or Actionable Guidance. This Project Proposes A Machine Learning–based Predictive Analysis Framework For The Early Detection Of Such Hidden Degradation Using Multivariate System Metrics Including CPU Usage, Memory Consumption, Disk I/O Wait, Network Latency, And Process Behavior Collected As Continuous Time-series Data. The Core Detection Engine Employs OmniAnomaly (Variational Autoencoder With GRU) To Learn Normal Temporal Behavior And Joint Distribution Of System Metrics In An Unsupervised Manner. Instead Of Relying On Labeled Failure Data, The Model Identifies Subtle Deviations Through Rising Reconstruction Error, Enabling Early Identification Of Gradual Performance Drift. To Make Predictions Interpretable, A SHAP-based Explainability Layer Quantifies Each Metric’s Contribution To The Anomaly Score, Revealing The Root Cause Of Degradation. A Rule-driven Recommendation Engine Maps Explainable Causes To Precise Corrective Actions. An Intelligent Alert System Validates Anomalies Using Adaptive Thresholds, Drift Trend Analysis, And SHAP Consistency Checks Before Issuing Multi-level Notifications. This Approach Enables Administrators To Detect, Understand, And Resolve Silent Performance Issues Proactively, Improving System Stability, Uptime, And Operational Efficiency.

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