Designing a distributed system involves overcoming major challenges such as heterogeneity, transparency, openness, concurrency, security, scalability, and fault tolerance, each of which must be addressed to build a reliable, extensible, and performant system.
This article presents a comprehensive overview of using graph representation learning and clustering, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market (malicious) accounts in virtual networks, discussing background, objectives, challenges such as isolation and heterogeneity, and evaluation results.
Meituan’s rollout of a private‑cloud service mesh—built on an Envoy data plane, custom control plane, and language‑agnostic SDKs—overcame compatibility, scaling, and heterogeneity hurdles through isolation, standardized governance runtime, data sharding, and centralized health checks, now supporting hundreds of services with sub‑millisecond latency and unified multi‑language management.
