Software Intelligence Models for Automated Application Management

David Lo

Software Intelligence Models for Automated Application Management

Author(s)	David Lo
Country	Singapore
Abstract	The rapid expansion of enterprise applications, cloud platforms, distributed services, mobile applications, and intelligent digital infrastructures has increased the complexity of software application management. Conventional application management practices frequently depend on manual monitoring, predefined operational rules, reactive troubleshooting, and fragmented administrative processes. Such approaches may become inefficient when applications operate across dynamic infrastructures and generate large volumes of operational telemetry. Software intelligence models provide an opportunity to transform application management by integrating artificial intelligence, machine learning, predictive analytics, anomaly detection, automated decision-making, observability, resource optimization, and intelligent incident response. This study examines software intelligence models for automated application management and develops an integrated conceptual framework for improving application reliability, performance, scalability, security, and operational efficiency. A conceptual descriptive research design was adopted using secondary literature related to intelligent software systems, application management, artificial intelligence, machine learning, AIOps, cloud computing, DevOps, observability, predictive maintenance, automated remediation, resource management, and software reliability. The analysis identifies application telemetry collection, data preprocessing, behavioral modeling, anomaly detection, performance prediction, root-cause analysis, intelligent decision-making, automated remediation, resource optimization, continuous monitoring, and learning-based improvement as major components of intelligent application management. The proposed framework integrates these components into a continuous management lifecycle beginning with application observation and ending with adaptive optimization. The findings indicate that intelligent application management can improve operational visibility by correlating information from logs, metrics, traces, infrastructure resources, user activities, application dependencies, and security events. Automated remediation can subsequently initiate predefined corrective actions, reducing response time and operational workload. Intelligent resource management can dynamically adjust computational resources according to application demand, thereby improving utilization and potentially reducing unnecessary infrastructure consumption.
Keywords	Software Intelligence, Automated Application Management, AIOps, Artificial Intelligence, Machine Learning, Application Monitoring, Anomaly Detection, Predictive Analytics, Automated Remediation, Software Reliability.
Field	Engineering
Published In	Volume 3, Issue 3, May-June 2021
Published On	2021-06-20

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American International Journal of Advanced Research and Development

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Software Intelligence Models for Automated Application Management

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