Serverless Observability: Monitoring and Debugging AWS Lambda Workflows at Scale

{{Abstract – Serverless computing simplifies cloud programming but introduces novel observability challenges because compute is ephemeral, auto-scaled, and highly distributed. This paper investigates pragmatic, scalable observability strategies for AWS Lambda–based serverless workflows, focusing on real-time monitoring, distributed tracing, and debugging. We critically evaluate AWS-native telemetry (CloudWatch, X-Ray) and vendor-neutral open-source options (OpenTelemetry), and we implement a proof-of-concept (PoC) hybrid pipeline using OpenTelemetry Collector + Jaeger and AWS native metrics. The PoC measures instrumentation overhead, trace completeness, debugging effectiveness, and cost proxies under controlled workloads. We present architectural patterns, a comparison of alternatives, and recommendations for FinServ and SaaS practitioners concerned with reliability, compliance, and cost. The evidence suggests that a carefully engineered hybrid observability model—one that leverages native metrics for low-latency SLOs and OpenTelemetry for end-to-end tracing—delivers the best balance of visibility, cost control, and portability. (arXiv, AWS Documentation, GitHub)}}

{{Conclusions. Serverless observability demands a pragmatic approach that balances integration, completeness, cost, and operational complexity. Our PoC and literature synthesis indicate that a hybrid model—CloudWatch metrics for SLO enforcement plus OpenTelemetry for end-to-end tracing—provides the most practical balance for production FinServ and SaaS deployments. The hybrid pattern reduces vendor dependence and yields richer debugging signals with manageable overhead when combined with adaptive sampling and observability-as-code.}}

{{References 1.E. Jonas et al., “Cloud Programming Simplified: A Berkeley View on Serverless Computing,” arXiv:1902.03383, Feb. 2019. (arXiv) 2.B. H. Sigelman et al., “Dapper, a large-scale distributed systems tracing infrastructure,” Google Research, 2010. (Google Research) 3.AWS, “Visualize Lambda function invocations using AWS X-Ray,” AWS Lambda Developer Guide. (AWS Documentation) 4.OpenTelemetry Project, “OpenTelemetry specification,” GitHub (spec and semantic conventions). (GitHub, CNCF) 5.“OpenTelemetry in Focus” — OpenTelemetry blog (2023 updates and metrics v1 work). (OpenTelemetry) 6.L. Schmid et al., “Benchmarking Serverless Cloud Function Workflows (SeBS-Flow),” arXiv:2410.03480, 2024. (arXiv) 7.A. Sandur et al., “Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing,” Proc. IEEE ICDE/2022 (preprint), 2022. (arXiv) 8.“Toward the Observability of Cloud-native Applications: Systematic Mapping Study,” IEEE Access (SMS overview), 2022/2023. (ResearchGate) 9.“A Reference Architecture for Observability and Compliance of CNAs,” OpenReview / workshop paper (2022). (OpenReview) Industry resources on observability pipelines, monitoring-as-code, and cost-aware telemetry (Chronosphere, Coralogix, vendor blogs). (Chronosphere, Coralogix)}}

{{author}}. “{{title}}.” International Journal of Computer Techniques (IJCT), Volume 12, Issue 5, September–October 2025. ISSN 2394-2231. Available at: https://ijctjournal.org/ Copy

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