diff --git a/README.md b/README.md
index 47e4f9d65389dec39c2b455497a0cb81e0bb7eb1..31db680c8abdacba5f126cdf13dda266f6ceb791 100755
--- a/README.md
+++ b/README.md
@@ -30,7 +30,7 @@ The goal of Paddle Serving is to provide high-performance, flexible and easy-to-
 - Integrate high-performance server-side inference engine paddle Inference and mobile-side engine paddle Lite. Models of other machine learning platforms (Caffe/TensorFlow/ONNX/PyTorch) can be migrated to paddle through [x2paddle](https://github.com/PaddlePaddle/X2Paddle).
 - There are two frameworks, namely high-performance C++ Serving and high-easy-to-use Python pipeline. The C++ Serving is based on the bRPC network framework to create a high-throughput, low-latency inference service, and its performance indicators are ahead of competing products. The Python pipeline is based on the gRPC/gRPC-Gateway network framework and the Python language to build a highly easy-to-use and high-throughput inference service. How to choose which one please see [Techinical Selection](doc/Serving_Design_EN.md#21-design-selection).
 - Support multiple [protocols](doc/C++_Serving/Inference_Protocols_CN.md) such as HTTP, gRPC, bRPC, and provide C++, Python, Java language SDK.
-- Design and implement a high-performance inference service framework for asynchronous pipelines based on directed acyclic graph (DAG), with features such as multi-model combination, asynchronous scheduling, concurrent inference, dynamic batch, multi-card multi-stream inference, etc.
+- Design and implement a high-performance inference service framework for asynchronous pipelines based on directed acyclic graph (DAG), with features such as multi-model combination, asynchronous scheduling, concurrent inference, dynamic batch, multi-card multi-stream inference, request cache, etc.
 - Adapt to a variety of commonly used computing hardwares, such as x86 (Intel) CPU, ARM CPU, Nvidia GPU, Kunlun XPU, HUAWEI Ascend 310/910, HYGON DCU、Nvidia Jetson etc. 
 - Integrate acceleration libraries of Intel MKLDNN and  Nvidia TensorRT, and low-precision and quantitative inference.
 - Provide a model security deployment solution, including encryption model deployment, and authentication mechanism, HTTPs security gateway, which is used in practice.
@@ -75,6 +75,7 @@ The first step is to call the model save interface to generate a model parameter
 - [Guide for RESTful/gRPC/bRPC APIs(Chinese)](doc/C++_Serving/Introduction_CN.md#42-多语言多协议Client)
 - [Infer on quantizative models](doc/Low_Precision_EN.md)
 - [Data format of classic models(Chinese)](doc/Process_data_CN.md)
+- [Prometheus(Chinese)](doc/Prometheus_CN.md)
 - [C++ Serving(Chinese)](doc/C++_Serving/Introduction_CN.md) 
   - [Protocols(Chinese)](doc/C++_Serving/Inference_Protocols_CN.md)
   - [Hot loading models](doc/C++_Serving/Hot_Loading_EN.md)
@@ -83,6 +84,7 @@ The first step is to call the model save interface to generate a model parameter
   - [Analyze and optimize performance(Chinese)](doc/C++_Serving/Performance_Tuning_CN.md)
   - [Benchmark(Chinese)](doc/C++_Serving/Benchmark_CN.md)
   - [Multiple models in series(Chinese)](doc/C++_Serving/2+_model.md)
+  - [Request Cache(Chinese)](doc/C++_Serving/Request_Cache_CN.md)
 - [Python Pipeline](doc/Python_Pipeline/Pipeline_Design_EN.md)
   - [Analyze and optimize performance](doc/Python_Pipeline/Performance_Tuning_EN.md)
   - [TensorRT dynamic Shape](doc/TensorRT_Dynamic_Shape_EN.md)
diff --git a/README_CN.md b/README_CN.md
index 324ad49b406ac0849f4d313012ff24763f289741..fefdfbc75a5b4bdc6dc77f2cc0b3f3f3d5a5f2c9 100755
--- a/README_CN.md
+++ b/README_CN.md
@@ -29,13 +29,14 @@ Paddle Serving依托深度学习框架PaddlePaddle旨在帮助深度学习开发
 - 集成高性能服务端推理引擎paddle Inference和移动端引擎paddle Lite，其他机器学习平台（Caffe/TensorFlow/ONNX/PyTorch）可通过[x2paddle](https://github.com/PaddlePaddle/X2Paddle)工具迁移模型
 - 具有高性能C++和高易用Python 2套框架。C++框架基于高性能bRPC网络框架打造高吞吐、低延迟的推理服务，性能领先竞品。Python框架基于gRPC/gRPC-Gateway网络框架和Python语言构建高易用、高吞吐推理服务框架。技术选型参考[技术选型](doc/Serving_Design_CN.md#21-设计选型)
 - 支持HTTP、gRPC、bRPC等多种[协议](doc/C++_Serving/Inference_Protocols_CN.md)；提供C++、Python、Java语言SDK
-- 设计并实现基于有向无环图(DAG)的异步流水线高性能推理框架，具有多模型组合、异步调度、并发推理、动态批量、多卡多流推理等特性
+- 设计并实现基于有向无环图(DAG)的异步流水线高性能推理框架，具有多模型组合、异步调度、并发推理、动态批量、多卡多流推理、请求缓存等特性
 - 适配x86(Intel) CPU、ARM CPU、Nvidia GPU、昆仑XPU、华为昇腾310/910、海光DCU、Nvidia Jetson等多种硬件
 - 集成Intel MKLDNN、Nvidia TensorRT加速库，以及低精度和量化推理
 - 提供一套模型安全部署解决方案，包括加密模型部署、鉴权校验、HTTPs安全网关，并在实际项目中应用
 - 支持云端部署，提供百度云智能云kubernetes集群部署Paddle Serving案例
 - 提供丰富的经典预模型部署示例，如PaddleOCR、PaddleClas、PaddleDetection、PaddleSeg、PaddleNLP、PaddleRec等套件，共计40+个预训练精品模型
 - 支持大规模稀疏参数索引模型分布式部署，具有多表、多分片、多副本、本地高频cache等特性、可单机或云端部署
+- 支持服务监控，提供基于普罗米修斯的性能数据统计及端口访问
 
 
 <h2 align="center">教程</h2>
@@ -70,6 +71,7 @@ Paddle Serving依托深度学习框架PaddlePaddle旨在帮助深度学习开发
 - [RESTful/gRPC/bRPC API指南](doc/C++_Serving/Introduction_CN.md#42-多语言多协议Client)
 - [低精度推理](doc/Low_Precision_CN.md)
 - [常见模型数据处理](doc/Process_data_CN.md)
+- [普罗米修斯](doc/Prometheus_CN.md)
 - [C++ Serving简介](doc/C++_Serving/Introduction_CN.md) 
   - [协议](doc/C++_Serving/Inference_Protocols_CN.md)
   - [模型热加载](doc/C++_Serving/Hot_Loading_CN.md)
@@ -78,6 +80,7 @@ Paddle Serving依托深度学习框架PaddlePaddle旨在帮助深度学习开发
   - [性能优化指南](doc/C++_Serving/Performance_Tuning_CN.md)
   - [性能指标](doc/C++_Serving/Benchmark_CN.md)
   - [多模型串联](doc/C++_Serving/2+_model.md)
+  - [请求缓存](doc/C++_Serving/Request_Cache_CN.md)
 - [Python Pipeline设计](doc/Python_Pipeline/Pipeline_Design_CN.md)
   - [性能优化指南](doc/Python_Pipeline/Performance_Tuning_CN.md)
   - [TensorRT动态shape](doc/TensorRT_Dynamic_Shape_CN.md)
diff --git a/doc/Prometheus_CN.md b/doc/Prometheus_CN.md
index 4807d8c7029b0f5252aa632a92511b762002c4ff..67c20b0246bd6092170ac86da7a4a3b01ee276bb 100644
--- a/doc/Prometheus_CN.md
+++ b/doc/Prometheus_CN.md
@@ -7,7 +7,7 @@ curl http://localhost:19393/metrics
 
 ## 配置使用
 
-### C+ Server
+### C++ Server
 
 对于 C++ Server 来说，启动服务时请添加如下参数