$ show QQ3221197721/cloudai-fusion
CloudAI Fusion is a cloud-native control plane for managing multi-cloud AI infrastructure, emphasizing verifiable auditability and cryptographic evidence. It unifies GPU scheduling, security red-teaming, FinOps, and GitOps under a single evidence ledger that records tamper-evident receipts for all control-plane actions. The architecture is polyglot, using Go for the high-performance control plane (API Server, Scheduler, Agent) and Python for AI/ML agents (anomaly detection, LLM orchestration). It integrates deeply with Kubernetes, supporting multi-tenancy, edge-cloud federation, and WebAssembly-based serverless workloads. The system distinguishes itself through a 'Verifiable Moat' architecture where subsystems are unified under a cryptographic ledger using Ed25519 signatures and Merkle transparency logs. It enforces 'honesty' by explicitly reporting backend modes (real vs. simulated) via a capability registry, preventing production deployments from relying on simulated backends. Key functionality includes verifiable model provenance, authorized red teaming with reproducible exploit witnesses, and cross-cluster failover attestation. The platform supports major cloud providers (AWS, Azure, GCP, Aliyun, Huawei, Tencent) through a standardized abstraction layer. While primarily an application, it contains significant data engineering and ML components, particularly in the `ai/` directory for RL-based scheduling and anomaly detection. These are integrated as features of the platform rather than standalone research artifacts. The codebase demonstrates high craftsmanship in distributed systems patterns, including Raft consensus, event-driven messaging, and plugin-based extensibility. However, the complexity of the cryptographic evidence layer and the mixed language stack introduce maintenance overhead.
The LLM identified 4 files worth highlighting for reuse:
Implements a hash-chained, append-only evidence ledger using Ed25519 signatures and Merkle roots. It provides tamper-evident receipts for control-plane actions, anchored to transparency logs (Rekor). This pattern is reusable for any system requiring cryptographic auditability and offline verification of data integrity.
Core scheduling engine that integrates GPU topology discovery with RL-based optimization (Q-learning). It handles capacity management, isolation, and predictive scaling. The logic is encapsulated enough to be adapted for other heterogeneous resource scheduling problems beyond AI workloads.
Unified LLM interface supporting multiple providers (OpenAI, DashScope, Ollama) with fallback logic and structured output parsing. It abstracts the complexity of different API contracts, making it reusable for any agent-based system requiring multi-provider LLM access.
Standardized abstraction layer for major cloud providers (AWS, Azure, GCP, etc.) implementing a common `Provider` interface. It handles cluster management and resource pricing uniformly, allowing the platform to be cloud-agnostic. This pattern is highly reusable for multi-cloud management tools.