Cloud-Native Network Monitoring: Tools, Architectures, And Best Practices

Cloud-native networking has transformed modern enterprise and service provider infrastructures by enabling highly dynamic, scalable, and distributed environments based on microservices, containers, and multi-cloud deployments. While these architectures improve agility and resource efficiency, they also introduce significant challenges in maintaining visibility, performance assurance, and security. Traditional network monitoring approaches are inadequate for handling ephemeral workloads, high-velocity telemetry, and complex inter-service communications. This paper presents a comprehensive review of cloud-native network monitoring, focusing on monitoring tools, architectural frameworks, and operational best practices suitable for modern cloud-native ecosystems. It systematically analyzes open-source and commercial monitoring solutions, including Prometheus, Grafana, OpenTelemetry, ELK Stack, and cloud-provider-native platforms, highlighting their roles in metrics collection, logging, and distributed tracing. The study further examines key architectural models such as centralized, distributed, and hybrid monitoring frameworks, as well as agent-based and agentless approaches, emphasizing scalability, fault tolerance, and integration with orchestration platforms like Kubernetes. Best practices for observability design, metric selection, alerting, and automated incident management are discussed in the context of DevOps and Site Reliability Engineering (SRE). Additionally, the paper identifies critical challenges related to scalability, hybrid and multi-cloud observability, security, and privacy, while outlining emerging research directions including AI/ML-driven monitoring, autonomous remediation, and edge observability. By consolidating tools, architectures, and operational strategies, this paper provides a structured reference for researchers and practitioners seeking to design, deploy, and optimize effective cloud-native network monitoring systems.