Feature/2026 real world test improvements

[morningstarxcdcode]

This pull request introduces a comprehensive suite of real-world edge AI scalability tests for OpenYurt, targeting 2026-level requirements. The changes add new end-to-end (e2e) test documentation and a fully implemented Ginkgo/Gomega-based test file to validate OpenYurt’s ability to support massive AI workloads, 6G-like network conditions, and robust self-healing under chaos scenarios. These tests are designed to ensure OpenYurt’s readiness for next-generation edge computing, focusing on scalability, resilience, and performance.

Key changes include:

New Test Documentation and Coverage

• Added 2026-IMPROVEMENTS.md outlining test goals, scenarios, metrics, and CI/CD integration for edge AI scalability, quantum-resistant security, sustainability, and multi-cloud/AI interoperability. This document provides clear instructions, benchmarks, and future enhancement plans.

AI Edge Scalability Test Implementation

• Introduced ai_edge_scalability_test.go which automates:
  • Deployment of up to 1000 AI inference pods with sub-1ms latency targets.
  • Simulation of 6G network conditions for ultra-low latency.
  • Measurement of throughput (10M+ inferences/sec) and concurrent request handling (50,000+).
  • Chaos engineering scenarios (network partitions, node failures) and verification of recovery/self-healing within 2 minutes.
  • Concurrent deployment of multiple AI model types (vision, NLP, speech, recommendation) to validate multi-model support.

Test Utility Functions and Metrics

• Provides helper functions for:
  • Creating Kubernetes deployments for AI workloads with resource constraints and edge node scheduling.
  • Simulating network chaos and measuring recovery time.
  • Simulating high concurrency and measuring throughput and latency metrics.
  • Waiting for deployments to become ready and cleaning up resources after tests.

Integration with OpenYurt E2E Config and Kubernetes APIs

• Utilizes OpenYurt’s e2e configuration and Kubernetes client libraries for namespace, deployment, and pod management, ensuring tests are fully automated and repeatable in CI/CD pipelines.

CI/CD and Future Roadmap

• Documents how to run these tests locally and in CI (GitHub

[Copilot]

Pull request overview

This PR introduces a comprehensive test suite for OpenYurt targeting 2026-level edge computing requirements, including AI scalability, quantum-resistant security, energy efficiency, and multi-cloud integration capabilities. The changes add four new e2e test files across new test directories (scalability, security, sustainability, integration) and comprehensive documentation.

Changes:

• New test framework for AI edge scalability with 6G network simulation and chaos engineering
• Quantum-resistant security tests implementing post-quantum cryptography standards (Kyber-768, Dilithium-2)
• Energy efficiency and sustainability tests for green computing optimization
• Multi-cloud and AI framework integration tests for hybrid cloud-edge deployments

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 9 comments.

Show a summary per file

File	Description
test/e2e/scalability/ai_edge_scalability_test.go	AI inference scalability tests with 6G network conditions and chaos scenarios (CRITICAL ISSUES)
test/e2e/security/quantum_resistant_test.go	Quantum-resistant cryptography and zero-trust security tests (CRITICAL ISSUES)
test/e2e/sustainability/energy_efficiency_test.go	Energy-aware scheduling and carbon footprint optimization tests
test/e2e/integration/multi_cloud_ai_test.go	Multi-cloud migration, AI framework, and 5G/6G network integration tests
test/e2e/2026-IMPROVEMENTS.md	Comprehensive documentation of test goals, benchmarks, and implementation details

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[morningstarxcdcode]

@luc99hen @rambohe-ch sir pls review it

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud