
Developed and enhanced Quality of Service (QoS) testing frameworks for the sonic-mgmt repository over a two-month period, focusing on the q3d platform. Leveraging Python and network programming skills, introduced expanded test configurations, CIR/PIR validation, and support for Q3D ASIC and PortChannel scenarios. Implemented utilities for port mapping and DSCP-to-queue mapping to ensure accurate traffic generation and validation, while adjusting test margins to reflect platform-specific characteristics. Enabled LossyQueueTest to extend validation into lossy queue scenarios, improving test coverage and reliability. Positioned the new frameworks for continuous integration, supporting broader hardware profiles and more robust Quality Assurance processes.
March 2026: Delivered a new Strict Priority QoS Testing Framework for sonic-mgmt, featuring CIR/PIR validation, Q3D ASIC and PortChannel support, and robust helper utilities for port mapping and DSCP-to-queue mapping. This work enhances QoS test coverage, reliability, and readiness for CI integration across hardware profiles.
March 2026: Delivered a new Strict Priority QoS Testing Framework for sonic-mgmt, featuring CIR/PIR validation, Q3D ASIC and PortChannel support, and robust helper utilities for port mapping and DSCP-to-queue mapping. This work enhances QoS test coverage, reliability, and readiness for CI integration across hardware profiles.
February 2026 monthly summary for sonic-mgmt: Implemented QoS Testing Framework Enhancements for the q3d platform, expanding test configurations, adjusting margins for q3d characteristics, and enabling LossyQueueTest to improve QoS validation and coverage. This work reduces risk in QoS deployments and provides clearer visibility into performance under varied queue scenarios.
February 2026 monthly summary for sonic-mgmt: Implemented QoS Testing Framework Enhancements for the q3d platform, expanding test configurations, adjusting margins for q3d characteristics, and enabling LossyQueueTest to improve QoS validation and coverage. This work reduces risk in QoS deployments and provides clearer visibility into performance under varied queue scenarios.

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