G
getanteon/anteon
Anteon
Anteon is a distributed load testing platform and automated performance testing suite designed to simulate high-traffic user scenarios and measure system performance across multiple global locations. It functions as an infrastructure anomaly detector and a service dependency mapper, providing a performance monitoring dashboard to track real-time resource usage across cluster instances.
The project distinguishes itself by combining distributed traffic generation with service dependency mapping to identify system bottlenecks through network-level tracing. It incorporates an automated validation system that evaluates response codes and data against success criteria to determine if system updates pass or fail.
The platform covers broad capability areas including cluster resource monitoring for CPU and memory tracking, system anomaly alerting, and the simulation of complex user workflows. It supports test design through CSV data injection and request parameterization, as well as post-test analysis with JSON result exports.
Features
- Distributed Load Generation - Spawns request workers across multiple global locations to simulate high-scale traffic and network latency.
- Load Testing Tools - Generates high volumes of traffic from multiple global locations to measure system performance under stress.
- Session State Management - Maintains cookies and captures response variables to facilitate stateful user workflows across sequential requests.
- Performance Success Metrics - Automatically determines test outcomes by calculating response time percentiles and failure counts against success criteria.
- API Performance Monitoring - Tracks real-time resource usage and response times to detect performance regressions and anomalies in API endpoints.
- Service Dependency Mapping - Analyzes network traffic flow and service interactions to generate visual maps of system dependencies and bottlenecks.
- Load Simulation Testing - Sends a specific number of requests to a target URL over a set duration to evaluate stability.
- Automated Test Suites - Provides a framework for executing complex user workflows with dynamic data and automated success validation.
- User Workflow Simulation - Runs sequences of dependent requests in a specific order to simulate complex real-world user workflows.
- Response Validation - Provides mechanisms for verifying that API responses meet predefined success criteria based on status codes and body content.
- API Response Validation - Automatically validates API response codes and data against success criteria to determine if system updates pass.
- Response Value Extraction - Extracts data from response bodies, headers, or cookies to be used in subsequent sequential requests.
- HTTP Session Simulations - Maintains cookies across request iterations to simulate users with persistent state.
- CSV Parameter Injections - Supports test design by mapping external CSV columns to request parameters for high-volume, diverse data simulation.
- Anomaly Detection - Identifies unusual patterns in system metrics and performance drops to proactively flag issues via notifications.
- Cluster Monitoring - Tracks real-time cluster health and resource utilization including CPU, memory, disk, and network usage.
- Time-Series Monitoring Systems - Collects and stores real-time CPU and memory metrics to correlate system resource usage with active load tests.
- Infrastructure Anomaly Detectors - Detects unusual resource spikes in the infrastructure and sends notifications to external communication channels.
- Real-Time Monitoring Dashboards - Provides a visual interface for tracking real-time CPU, memory, and network resource usage across cluster instances.
- Test Data Injection - Enables mapping external CSV file columns to request parameters and payloads for diverse test data.
- Workflow Sequence Simulations - Simulates complex real-world user workflows by running sequences of dependent requests with dynamic data.
- Test Parameterization - Injects random dynamic values or environment variables into URLs and headers to simulate diverse traffic.
- Performance Success Criteria - Automates pass/fail outcomes based on response time percentiles and failure counts across test runs.
- Monitoring and Observability - Kubernetes monitoring and performance testing platform.
