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All Journal The Eastasouth Journal of Information System and Computer Science
Mohit Bajpai
Independent researcher, Alpharetta, GA, USA
Computer Science & IT

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From Observability to Closed-Loop AIOps: Data-Driven Automation for Secure and Resilient Network Operations Mohit Bajpai
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1090

Abstract

Modern enterprise and service-provider networks are now distributed across cloud, edge, software-defined data centers, mobile access, Internet of Things (IoT), and hybrid work environments. The operational challenge is no longer limited to device availability; teams must interpret high-volume telemetry, fast-changing application paths, user-experience signals, identity context, security events, and configuration drift at machine speed. This updated article expands the original discussion of AI Ops, machine learning, observability, and network security by adding a data-centered reference architecture, operational metrics, model-selection considerations, security controls, deployment phases, and governance requirements. The article explains how telemetry from SNMP, streaming telemetry, NetFlow/IPFIX, syslog, OpenTelemetry, endpoint logs, cloud logs, configuration repositories, and security tools can be converted into actionable intelligence through anomaly detection, forecasting, causal correlation, risk scoring, and policy-based automation. It also positions closed-loop AIOps as a practical operating model that improves mean time to detect, mean time to acknowledge, mean time to resolve, service-level compliance, capacity planning, and security response while preserving human approval for high-risk actions.
Autonomous Network Troubleshooting with AIOps and Machine Learning: A Data-Driven Architecture for Correlation, Prediction, and Remediation Mohit Bajpai
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1101

Abstract

Modern networks carry business-critical traffic across data centers, cloud platforms, branch locations, APIs, telecom circuits, security gateways, and software-defined overlays. In this environment, traditional troubleshooting is no longer limited by the availability of alarms; it is limited by the volume, fragmentation, and operational interpretation of telemetry. This updated paper presents an expanded AIOps and machine learning framework for automating network troubleshooting through telemetry ingestion, data enrichment, anomaly detection, event correlation, root-cause ranking, predictive risk scoring, and governed closed-loop remediation. The paper extends the original architecture by adding data governance, model lifecycle management, explainability, human approval gates, operational KPIs, and security controls. It also introduces a telecommunications implementation scenario in which Remedy tickets, Kafka streams, Kong APIs, topology data, and AIOps model outputs are combined to reduce alert noise, accelerate diagnosis, and improve network resilience. The proposed approach is not intended to replace network engineers; rather, it converts repetitive investigation patterns into repeatable, auditable, and continuously improving operational workflows.
Agentic AI-Enhanced Network Performance Monitoring and Diagnostic Analysis for Site Reliability Engineering Mohit Bajpai
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1102

Abstract

Network performance monitoring and diagnostic analysis (NPMD) is becoming a core reliability discipline for modern distributed systems because cloud applications, hybrid connectivity, software-defined networking, and multi-region dependency chains can turn small network degradations into visible service incidents. The original paper explained NPMD through Site Reliability Engineering (SRE) concepts such as service level indicators (SLIs), service level objectives (SLOs), and non-functional requirements. This updated version expands the work with a data-driven operating model, stronger references, explicit table and figure captions, and an Agentic AI solution pattern for bounded autonomous diagnosis and remediation. The proposed approach combines telemetry pipelines, SLO evaluation, topology and change evidence, retrieval-augmented diagnostic reasoning, runbook-constrained tool execution, and human approval controls. The paper treats AI as an operational assistant rather than an uncontrolled replacement for SRE judgment: the agent can summarize evidence, correlate probable causes, recommend remediation, and execute only low-risk approved actions while preserving auditability. The result is a practical framework for reducing alert noise, improving time to detect, accelerating incident triage, and strengthening post-incident learning without relying on unsupported claims or unverifiable performance numbers.
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