Implementing a Comprehensive Metrics, Logs, and Traces Ingestion Pipeline in Kubernetes Using Open-Source Solutions

Description

In today’s complex, distributed systems, effective monitoring is essential for understanding system behavior, identifying performance bottlenecks, and troubleshooting issues. This thesis aims to design, implement, and evaluate a robust metrics, logs, and traces ingestion pipeline within a Kubernetes environment, leveraging popular open-source tools.

The current monitoring infrastructure, relying solely on Prometheus, provides valuable metrics but lacks the comprehensive visibility required for modern applications. By incorporating Grafana Tempo for distributed tracing and Loki for log aggregation, we aim to create a more holistic monitoring solution that captures a wider range of data.

Goals

• Deploy and configure Grafana Tempo, Loki, and potentially Mimir within the SESAR Lab Kubernetes cluster.
• Integrate these tools with existing monitoring systems, such as Prometheus, to ensure seamless data flow.
• Develop sample applications or adapt existing ones to generate metrics, logs, and traces for testing and evaluation.
• Measure the performance and scalability of the implemented pipeline under various workloads.
• Analyze the collected data to identify potential improvements and optimizations.
• Explore the feasibility of porting Moon Cloud probes to output metrics to Tempo, enhancing the monitoring capabilities.

Prerequisites

• Familiarity with Kubernetes concepts, including deployments, services, and namespaces.
• Understanding of monitoring principles and tools like Prometheus.
• Basic knowledge of distributed systems and microservices architecture.

Additional Considerations:

• Data Privacy and Security: Ensure compliance with data protection regulations and implement appropriate security measures.
• Long-Term Storage: Consider strategies for long-term storage of metrics, logs, and traces to support analysis and troubleshooting over time.
• Alerting and Notification: Explore integration with alerting systems to proactively notify stakeholders of critical events.

By successfully completing this thesis, we will contribute to the development of a more comprehensive and efficient monitoring solution for Kubernetes-based systems, enabling better performance, troubleshooting, and decision-making.