Cloud Stateless System Performance Metrics and Status

We utilized Digital Ocean's cloud service, setting up three Linux virtual machines, each with 1vCPU, 1GB of memory, and a 10GB disk. The architecture included an API gateway for routing requests to a stateless application service backed by a database for storing application data. The application operates the service under a fluctuating workload generated by a load-testing script to simulate real-world usage scenarios. The target source or the application service is integrated with Prometheus, a monitoring tool for gathering system metrics. To extract data from Prometheus, we devised a custom script capable of tapping into its local storage, thereby collecting resource utilization and performance metrics. The resulting dataset encompasses roughly 8,000 data points gathered at 5-second intervals. These data points span a variety of metrics: CPU and memory usage (in percentages), network traffic (inbound and outbound rates in GB/s or MB/s), transactions per second (TPS), and response times (in seconds or milliseconds). A critical aspect of our dataset was the real-time health status of the system, assessed through HTTP response codes. Using our custom script, we monitored these codes; if predefined error codes (5xx) were detected, the system was marked as unhealthy. In all other scenarios, the system was deemed healthy.