fix: update llm-d-inference-sim in arch (#45)

Signed-off-by: rambohe-ch <rambohe.ch@gmail.com>

Author: rambohe-ch

README.md

@@ -10,12 +10,12 @@ This project evaluates a production inference stack built on top of existing OSS
 
 - **[Istio Gateway](https://istio.io/latest/docs/tasks/traffic-management/ingress/gateway-api/)** — Entry point for all inference requests. Routes client requests (e.g., `POST /v1/chat/completions`) through the stack.
 - **[llm-gateway-auth](https://github.com/kaito-project/llm-gateway-auth)** — ext_authz API-key authorization filter. Validates the `Authorization: Bearer <token>` header against an `APIKey` custom resource resolved from the request's `Host` subdomain (`<namespace>.gw.example.com`) before any routing or model dispatch happens. Ships two components — `apikey-operator` (reconciles `APIKey` CRs into per-namespace Secrets) and `apikey-authz` (the ext_authz dataplane).
-- **[Body-based Routing (BBR)](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md)** — Parses request body to extract the model name and injects the `X-Gateway-Model-Name` header, enabling model-level routing.
+- **[body-based routing (BBR)](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md)** — Parses request body to extract the model name and injects the `X-Gateway-Model-Name` header, enabling model-level routing.
 - **[llm-d-inference-scheduler (EPP)](https://github.com/llm-d/llm-d-inference-scheduler)** — Per-model Endpoint Picker (image `mcr.microsoft.com/oss/v2/llm-d/llm-d-inference-scheduler`). Performs KV-cache aware routing by injecting the `x-gateway-destination-endpoint` header, directing requests to the optimal inference pod.
 - **[Kaito InferenceSet](https://github.com/kaito-project/kaito)** — Manages groups of vLLM inference pods. Multiple InferenceSets (e.g., Model-A, Model-B) can run different models simultaneously.
-- **[vLLM Inference Pods](https://github.com/vllm-project/vllm)** — Serve model inference requests. On CPU-only E2E clusters, the real vLLM container is replaced by a **shadow pod** running [`llm-d-inference-sim`](https://github.com/llm-d/llm-d-inference-sim) (image `ghcr.io/llm-d/llm-d-inference-sim`), a lightweight vLLM-compatible simulator that exposes the same OpenAI API and `vllm:*` Prometheus metrics. See [`pkg/gpu-node-mocker/README.md`](pkg/gpu-node-mocker/README.md) for the original-pod ↔ shadow-pod mechanism.
-- **[Kaito-Keda-Scaler](https://github.com/kaito-project/keda-kaito-scaler)** — Metric-based autoscaler built on [KEDA](https://keda.sh/) that scales vLLM inference pods up and down based on workload metrics.
-- **Mocked GPU Nodes / CPU Nodes** — Infrastructure layer providing compute resources for inference workloads. The `gpu-node-mocker` controller (E2E-only) fakes GPU nodes on CPU-only clusters and runs the `llm-d-inference-sim` shadow pods on real CPU nodes.
+- **[vLLM Inference Pods(llm-d-inference-sim)](https://github.com/llm-d/llm-d-inference-sim)** — Serve model inference requests. On CPU-only E2E clusters, the real vLLM container is replaced by a **shadow pod** running llm-d-inference-sim (image `ghcr.io/llm-d/llm-d-inference-sim`), a lightweight vLLM-compatible simulator that exposes the same OpenAI API and `vllm:*` Prometheus metrics. See [`pkg/gpu-node-mocker/README.md`](pkg/gpu-node-mocker/README.md) for the original-pod ↔ shadow-pod mechanism.
+- **[keda-kaito-scaler](https://github.com/kaito-project/keda-kaito-scaler)** — Metric-based autoscaler built on [KEDA](https://keda.sh/) that scales vLLM inference pods up and down based on workload metrics.
+- **[Mocked GPU Nodes](https://github.com/kaito-project/production-stack/blob/main/pkg/gpu-node-mocker/README.md) / CPU Nodes** — Infrastructure layer providing compute resources for inference workloads. The `gpu-node-mocker` controller (E2E-only) fakes GPU nodes on CPU-only clusters and runs the `llm-d-inference-sim` shadow pods on real CPU nodes.
 
 ## Resource Management