
Abhishek contributed to the sonic-net/sonic-mgmt repository by developing and enhancing Quality of Service (QoS) testing frameworks over a two-month period. He implemented new test configurations for the q3d platform, including expanded queue scenarios and precise margin adjustments to improve test accuracy. Leveraging Python scripting and network programming skills, Abhishek introduced strict priority QoS validation with CIR/PIR support, integrated helper utilities for port and DSCP-to-queue mapping, and enabled robust traffic generation. His work increased test coverage and reliability, positioned the framework for CI integration, and addressed the complexities of validating QoS across diverse hardware profiles without introducing new bugs.
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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