Runtime metrics exposed from experimental services
Project Detail
Distributed Systems Monitoring Experiment
An observability and load-testing experiment for microservices pressure, Prometheus metrics, Grafana dashboards, Locust workloads, and autoscaling research.
Distributed systems lab
Project Overview
This page expands the case-study summary into a clearer view of scope, architecture, workflow, and technical signals.
Stack
Python, Prometheus, Grafana, Locust, microservices experiments
- A microservices environment needed baseline observability, load testing, and a research path toward intelligent scaling decisions.
- Established a public distributed-systems lab for monitoring, load behavior, and intelligent scaling research.
Features
Functional Scope
The project scope is framed around real product and operations behavior rather than a surface-level screen list.
Prometheus collection and Grafana visualization
Repeatable Locust load profiles
Research foundation for future autoscaling policy work
Engineering
Technical Signals
These signals show the implementation concerns that matter when a system moves beyond a prototype.
Engineering Signal
Workload generation separated from telemetry capture
Engineering Signal
Dashboarding used to identify pressure points
Engineering Signal
Baseline load profiles created before scaling experiments
Feedback loop designed
Feedback loop designed for intelligent scaling research
Workflow
How The System Moves
The strongest project pages explain what happens to state as users, admins, workers, and services interact.
- Services expose runtime metrics.
- Prometheus collects behavior under load.
- Locust generates repeatable traffic patterns.
- Grafana dashboards reveal latency, saturation, and scaling signals.
Case Study
Architecture Breakdown
The original systems-delivered breakdown remains available here for a compact architecture view.
Distributed Systems Monitoring Experiment
View ProjectProblem Statement
A microservices environment needed baseline observability, load testing, and a research path toward intelligent scaling decisions.
Architecture Overview
Python-based experimental services with Prometheus metrics collection, Grafana dashboards, Locust load testing, and a foundation for RL-based autoscaling research.
Data Flow Explanation
Services expose runtime metrics, Prometheus collects system behavior, Grafana visualizes pressure points, and Locust generates repeatable load patterns for scaling experiments.
Engineering Decisions
The experiment separates workload generation, telemetry capture, dashboarding, and scaling research so each part can be measured and changed independently.
Scaling Strategy
Baseline load profiles and metrics create the feedback loop needed for future autoscaling policy work and capacity experiments.
Outcome
Established a public distributed-systems lab for monitoring, load behavior, and intelligent scaling research.