Citation:

Abhishek Gupta, 2021. "Scalable Distributed Tracing and Performance Optimization in Microservices", ESP Journal of Engineering & Technology Advancements 1(1): 210-224.

Abstract:

Distributed tracing and performance optimization are two important factors required to keep microservices-based architecture flexible and fault-tolerant. However, as the adoption of microservices grows within organizations, monitoring and dealing with latency, issues, and inter-service coupling arises. This paper discusses the approach to doing distributed tracing or distributed logging in a system with one or more microservices and as explores ways to improve the efficiency of the same. We use Open Telemetry for tracing efforts, Kubernetes for scalable deployment, and machine learning for anomaly detection. Latency and response rate are observed, along with details of service interdependencies. This work provides improved system transparency, MTTR, and resource utilization. Conclusions, approaches, and guidance on its application are offered, thus making the material exceptionally valuable to system architects and developers who work with microservices within cloud platforms.

References:

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Keywords:

Distributed Tracing, Microservices, Performance Optimization, OpenTelemetry, Kubernetes, Anomaly Detection, Observability, MTTR, Cloud Computing.