diff --git a/benchmarks/compression/README.md b/benchmarks/compression/README.md
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+# Compression Benchmark
+
+The compression benchmark measures the performance of a serverless platform for the task of file compression. The benchmark uses the zlib library to compress and decompress input files. A specific file can be specified using the `--def_file` flag, however if not specified, the benchmark uses a default file. 
+
+The functionality is implemented in Python. The function is invoked using gRPC.
+
+## Running this benchmark locally (using docker)
+
+The detailed and general description how to run benchmarks local you can find [here](../../docs/running_locally.md). The following steps show it on the compression-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+### Invoke once
+2. Start the function with docker-compose
+   ```bash
+   docker-compose -f ./yamls/docker-compose/dc-compression-python.yaml up
+   ```
+3. In a new terminal, invoke the interface function with grpcurl.
+   ```bash
+   ./tools/bin/grpcurl -plaintext localhost:50000 helloworld.Greeter.SayHello
+   ```
+### Invoke multiple times
+2. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "localhost" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 50000 -dbg -time 10 -rps 1
+   ```
+
+## Running this benchmark (using knative)
+
+The detailed and general description on how to run benchmarks on knative clusters you can find [here](../../docs/running_benchmarks.md). The following steps show it on the compression-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+2. Start the function with knative
+   ```bash
+   kubectl apply -f ./yamls/knative/kn-compression-python.yaml
+   ```
+3. **Note the URL provided in the output. The part without the `http://` we'll call `$URL`. Replace any instance of `$URL` in the code below with it.**
+### Invoke once
+4. In a new terminal, invoke the interface function with test-client.
+   ```bash
+   ./test-client --addr $URL:80 --name "Example text for Compression"
+   ```
+### Invoke multiple times
+4. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "$URL" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 80 -dbg -time 10 -rps 1
+   ```
+## Tracing
+
+This Benchmark does not support distributed tracing for now.
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diff --git a/benchmarks/image-rotate/README.md b/benchmarks/image-rotate/README.md
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+# Image Rotate Benchmark
+
+The image rotate benchmark rotates an input image by 90 degrees. An input image can be specified, but if nothing is given, a default image is used. This benchmark also utilises and depends on a database to store the images that can be used, which in turn uses MongoDB. 
+
+The `init-database.go` script runs when starting the function and populates the database with the images from the `images` folder.
+
+The functionality is implemented in two runtimes, namely Go and Python. The function is invoked using gRPC.
+
+## Running this benchmark locally (using docker)
+
+The detailed and general description how to run benchmarks local you can find [here](../../docs/running_locally.md). The following steps show it on the image-rotate-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+### Invoke once
+2. Start the function with docker-compose
+   ```bash
+   docker-compose -f ./yamls/docker-compose/dc-image-rotate-python.yaml up
+   ```
+3. In a new terminal, invoke the interface function with grpcurl.
+   ```bash
+   ./tools/bin/grpcurl -plaintext localhost:50000 helloworld.Greeter.SayHello
+   ```
+### Invoke multiple times
+2. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "localhost" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 50000 -dbg -time 10 -rps 1
+   ```
+
+## Running this benchmark (using knative)
+
+The detailed and general description on how to run benchmarks on knative clusters you can find [here](../../docs/running_benchmarks.md). The following steps show it on the image-rotate-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+2. Initialise the database and start the function with knative
+   ```bash
+   kubectl apply -f ./yamls/knative/image-rotate-database.yaml
+   kubectl apply -f ./yamls/knative/kn-image-rotate-python.yaml
+   ```
+3. **Note the URL provided in the output. The part without the `http://` we'll call `$URL`. Replace any instance of `$URL` in the code below with it.**
+### Invoke once
+4. In a new terminal, invoke the interface function with test-client.
+   ```bash
+   ./test-client --addr $URL:80 --name "Example text for Image-rotate"
+   ```
+### Invoke multiple times
+4. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "$URL" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 80 -dbg -time 10 -rps 1
+   ```
+## Tracing
+
+This Benchmark does not support distributed tracing for now.
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diff --git a/benchmarks/rnn-serving/README.md b/benchmarks/rnn-serving/README.md
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+# RNN Serving Benchmark
+
+The RNN serving benchmark generates a string using an RNN model given a specific language to generate the string in. An language can be specified as an input, but if nothing is given, a default language is chosen, either at random or uniquely using the input generator.  
+
+The functionality is implemented in Python. The function is invoked using gRPC.
+
+## Running this benchmark locally (using docker)
+
+The detailed and general description how to run benchmarks local you can find [here](../../docs/running_locally.md). The following steps show it on the rnn-serving-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+### Invoke once
+2. Start the function with docker-compose
+   ```bash
+   docker-compose -f ./yamls/docker-compose/dc-rnn-serving-python.yaml up
+   ```
+3. In a new terminal, invoke the interface function with grpcurl.
+   ```bash
+   ./tools/bin/grpcurl -plaintext localhost:50000 helloworld.Greeter.SayHello
+   ```
+### Invoke multiple times
+2. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "localhost" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 50000 -dbg -time 10 -rps 1
+   ```
+
+## Running this benchmark (using knative)
+
+The detailed and general description on how to run benchmarks on knative clusters you can find [here](../../docs/running_benchmarks.md). The following steps show it on the rnn-serving-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+2. Initialise the database and start the function with knative
+   ```bash
+   kubectl apply -f ./yamls/knative/kn-rnn-serving-python.yaml
+   ```
+3. **Note the URL provided in the output. The part without the `http://` we'll call `$URL`. Replace any instance of `$URL` in the code below with it.**
+### Invoke once
+4. In a new terminal, invoke the interface function with test-client.
+   ```bash
+   ./test-client --addr $URL:80 --name "Example text for rnn-serving"
+   ```
+### Invoke multiple times
+4. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "$URL" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 80 -dbg -time 10 -rps 1
+   ```
+## Tracing
+
+This Benchmark does not support distributed tracing for now.
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diff --git a/benchmarks/video-analytics-standalone/README.md b/benchmarks/video-analytics-standalone/README.md
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+# Video Analytics Standalone Benchmark
+
+The video analyics standalone benchmark preprocesses an input video and runs an object detection model (squeezenet) on the video. An input video can be specified, but if nothing is given, a default video is used. This benchmark also utilises and depends on a database to store the videos that can be used, which in turn uses MongoDB. 
+
+The `init-database.go` script runs when starting the function and populates the database with the videos from the `videos` folder.
+
+The functionality is implemented in Python. The function is invoked using gRPC.
+
+## Running this benchmark locally (using docker)
+
+The detailed and general description how to run benchmarks local you can find [here](../../docs/running_locally.md). The following steps show it on the video-analytics-standalone-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+### Invoke once
+2. Start the function with docker-compose
+   ```bash
+   docker-compose -f ./yamls/docker-compose/dc-video-analytics-standalone-python.yaml up
+   ```
+3. In a new terminal, invoke the interface function with grpcurl.
+   ```bash
+   ./tools/bin/grpcurl -plaintext localhost:50000 helloworld.Greeter.SayHello
+   ```
+### Invoke multiple times
+2. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "localhost" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 50000 -dbg -time 10 -rps 1
+   ```
+
+## Running this benchmark (using knative)
+
+The detailed and general description on how to run benchmarks on knative clusters you can find [here](../../docs/running_benchmarks.md). The following steps show it on the video-analytics-standalone-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+2. Initialise the database and start the function with knative
+   ```bash
+   kubectl apply -f ./yamls/knative/video-analytics-standalone-database.yaml
+   kubectl apply -f ./yamls/knative/kn-video-analytics-standalone-python.yaml
+   ```
+3. **Note the URL provided in the output. The part without the `http://` we'll call `$URL`. Replace any instance of `$URL` in the code below with it.**
+### Invoke once
+4. In a new terminal, invoke the interface function with test-client.
+   ```bash
+   ./test-client --addr $URL:80 --name "Example text for video analytics standalone"
+   ```
+### Invoke multiple times
+4. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "$URL" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 80 -dbg -time 10 -rps 1
+   ```
+## Tracing
+
+This Benchmark does not support distributed tracing for now.
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diff --git a/benchmarks/video-processing/README.md b/benchmarks/video-processing/README.md
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+# Video Processing Benchmark
+
+The video processing benchmark converts an input video to grayscale. An input video can be specified, but if nothing is given, a default video is used. This benchmark also utilises and depends on a database to store the videos that can be used, which in turn uses MongoDB. 
+
+The `init-database.go` script runs when starting the function and populates the database with the videos from the `videos` folder.
+
+The functionality is implemented in Python. The function is invoked using gRPC.
+
+## Running this benchmark locally (using docker)
+
+The detailed and general description how to run benchmarks local you can find [here](../../docs/running_locally.md). The following steps show it on the video-processing-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+### Invoke once
+2. Start the function with docker-compose
+   ```bash
+   docker-compose -f ./yamls/docker-compose/dc-video-processing-python.yaml up
+   ```
+3. In a new terminal, invoke the interface function with grpcurl.
+   ```bash
+   ./tools/bin/grpcurl -plaintext localhost:50000 helloworld.Greeter.SayHello
+   ```
+### Invoke multiple times
+2. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "localhost" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 50000 -dbg -time 10 -rps 1
+   ```
+
+## Running this benchmark (using knative)
+
+The detailed and general description on how to run benchmarks on knative clusters you can find [here](../../docs/running_benchmarks.md). The following steps show it on the video-processing-python function.
+1. Build or pull the function images using `make all-image` or `make pull`.
+2. Initialise the database and start the function with knative
+   ```bash
+   kubectl apply -f ./yamls/knative/video-processing-database.yaml
+   kubectl apply -f ./yamls/knative/kn-video-processing-python.yaml
+   ```
+3. **Note the URL provided in the output. The part without the `http://` we'll call `$URL`. Replace any instance of `$URL` in the code below with it.**
+### Invoke once
+4. In a new terminal, invoke the interface function with test-client.
+   ```bash
+   ./test-client --addr $URL:80 --name "Example text for Video-processing"
+   ```
+### Invoke multiple times
+4. Run the invoker
+   ```bash
+   # build the invoker binary
+   cd ../../tools/invoker
+   make invoker
+
+   # Specify the hostname through "endpoints.json"
+   echo '[ { "hostname": "$URL" } ]' > endpoints.json
+
+   # Start the invoker with a chosen RPS rate and time
+   ./invoker -port 80 -dbg -time 10 -rps 1
+   ```
+## Tracing
+
+This Benchmark does not support distributed tracing for now.
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