IntelliOps: A Generic Multi-Source Monitoring Framework with Predictive Analytics for Enterprise Infrastructure
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Abstract
This paper presents IntelliOps, a novel monitoring framework that integrates multi-source system monitoring with predictive analytics capabilities for financial technology infrastructure. The proposed framework aggregates performance metrics from multiple monitoring platforms and consolidates them through a unified API, providing comprehensive visibility into both hardware and software performance metrics. IntelliOps introduces an innovative approach by synthesizing traditional monitoring methodologies with advanced machine learning techniques, incorporating time series predictive models (LSTM, GRU, RNN) and contemporary forecasting libraries for anomaly detection and predictive maintenance.
The framework's architecture consists of three primary components: (1) a centralized data collection system that integrates heterogeneous monitoring sources, (2) an analytical engine that processes infrastructure and application-level metrics, and (3) a machine learning pipeline that performs predictive analysis on the aggregated data. Our implementation analyzes a longitudinal dataset spanning over one year from a large-scale fintech platform, encompassing metrics such as multi-layer response times (caching, message queuing, runtime environment, databases), request volumes, error rates, and deployment events.
Experimental results demonstrate the framework's efficacy in anomaly detection and predictive maintenance, achieving high accuracy across diverse datasets. The evaluation reveals that our hybrid methodology, incorporating both supervised and unsupervised learning techniques, yields superior performance in risk segmentation and anomaly detection compared to conventional threshold-based monitoring systems. Additionally, the integration of modern time series analysis techniques with classical statistical models enables robust detection of seasonal patterns and trends, facilitating proactive infrastructure management.
This research advances the field of systems monitoring by providing a structured methodology for implementing deep learning models in targeted monitoring scenarios, thereby enhancing system performance and mitigating potential disruptions across diverse operational environments. The framework's adaptability and scalability make it particularly suitable for complex financial technology infrastructures where system reliability and performance are paramount.
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