Dragonfly is a CNCF Graduated project that provides a P2P-based file distribution system. Originally built for container image distribution at Alibaba-scale (processing billions of requests daily), Dragonfly turns every downloading node into a seed for its peers. Core Architecture: Figure 1: End-to-end flow of the P2P model distribution in Dragonfly. The Seed Peer fetches the model from the origin hub once (Step 1), the Dragonfly Scheduler computes the P2P topology (Step 3), and GPU nodes share pieces via micro-task distribution (Step 5) — reducing origin traffic from 26 TB to ~130 GB across a
Peer-to-Peer acceleration for AI model distribution with Dragonfly… Each URL scheme http, s3, gs, hf, modelscope, etc. maps to a backend that implements the Backend trait: tonic::async trait pub trait Backend { fn scheme &self - String; async fn stat &self, request: StatRequest - Result ; async fn get &self, request: GetRequest - Result ; async fn put &self, reques… …
… It says “If you want to be a transformation filter, you must implement these functions.” pub trait TransformationOps { // Add a new header to the request appends if header exists fn add request header &mut self, key: &str, value: & u8 - bool; // Set a header overwrites if it exists fn set request h… …
… A single request may observe inconsistent configuration state if a reload occurs mid-flight. Swift Configuration was built to address these gaps. …
… The AI industry rediscovered something the security industry should have built decades ago. The question is why we’re still running production workloads – the ones handling customer data, financial transactions, and critical infrastructure – on shared kernels with less isolation than a browser tab. …
… The sync-wave annotation ensures Kyverno is always installed before any policies are applied. infra-services/kyverno.yaml apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: kyverno namespace: argocd annotations: argocd.argoproj.io/sync-wave: "1" argocd.argoproj.io/compare-options: S… …
… Self-service workflows : Platforms that empower teams to operate independently require careful design, informed by the needs and expectations of diverse users. …
… Until one memory leak poisons the whole process. Until your deployment pipeline means everything or nothing deploys at once, because they’re the same thing. Cloud-native architecture is built around a fundamentally different assumption: things will break . …