The architect's guide to the AIoT - part 2

Building a real-world AIoT application

In part 1 of our architect's guide to the AIoT series, we explored the AIoT (Artificial Intelligence of Things) problem space, the emergent behaviors, and the architecturally significant challenges. We learned how to address them using AIoT patterns and comprehensive reference architecture. In this post, I will show you how to apply the principles and patterns of this reference architecture to build a real-world AIoT application that can run on resource-constrained edge devices.

AIoT reference implementation

While the reference architecture formalizes the recurring scenarios and repeatable best practices into abstract AIoT patterns, the reference implementation offers concrete archetypes that can be used as foundational building blocks for any AIoT application. In this implementation, I have attempted to maximize the use of open-source projects, however, in certain areas, none existed, so I wrote my own. I have coded these unopinionated modules with a deliberate openness to both extension and modification.

The reference implementation can be used as a collection of individual reusable libraries and templates, or as a unified application framework.

The reference implementation is organized into two sections:

1. Reference Infrastructure : This is the infrastructure aspect of the reference implementation and is built using the technology mappings described below.
2. Reference Application : This is the application aspect of the reference implementation that shows you how to build a "Real-world" AIoT solution on the reference infrastructure. The reference application is discussed in part 3 of our AIoT series.

The reference infrastructure

Technology mappings - MLOps and platform services

The core platform and MLOps services of the reference infrastructure use various CNCF projects from the Kubernetes ecosystem such as K3S, Argo, Longhorn, and Strimzi along with custom-coded modules in Go and Python. Here is the complete list of the mappings.

Technology mappings - application services

The AIoT application services, which are covered in detail in the next post in this AIoT series, primarily comprise custom-coded modules in C++, Python, and Go.

Infrastructure hardware specifications

Each infrastructure tier of this implementation uses a particular type of hardware and AI acceleration to ensure the resource availability, scalability, security, and durability guarantees of the tier are met. Each tier can independently scale and fail, enabling services on each tier to be deployed, managed, and secured independently. The hardware and OS specifications for each tier are listed here:

I will now show you how to configure each tier and prepare it to host an AIoT application.

Infrastructure configuration

Configuring the "Things" tier

The concrete implementation of this tier runs on an ESP32 SoC. The next post gets into the details of the hardware setup.

Configuring the inference tier

The concrete implementation of this tier runs on a cluster of three Coral Dev Boards and an ESP32 SoC. This tier hosts the following services:

• On Coral Dev Boards:
  • A PyCoral TFLite logistic regression module
  • A Go streaming API sidecar for context aware inferencing
• On ESP32 SoC:
  • A TFLM C++ module

The cluster of TPU Dev boards are ARM devices running Mendel Linux. These devices host the TFLite PyCoral modules. We will first install the latest Linux Mendel OS on the Dev Boards by following these steps: (Note: These steps are specific to the macOS)

1. Install ADB tools on your laptop or PC

   bash brew install android-platform-tools

2. Install the CP210x USB to UART Bridge VCP Drivers
3. Use a USB-micro-B cable and connect to the serial console port of the Dev Board

4. Use the serial terminal at 115200 baud to connect to the device

   screen /dev/tty.SLAB_USBtoUART 115200

5. Flash the latest firmware on the Coral Dev Board by following these instructions.
6. Change the hostname of each of the Coral Dev Boards to agentnode-coral-tpu1, agentnode-coral-tpu2 and agentnode-coral-tpu3.

Configuring the platform tier

The concrete implementation of this tier runs a cluster of two Raspberry Pi devices and a NVIDIA Jetson Nano device.

• The Jetson Nano device hosts the MLOps services that:
  • Runs extract, train, drift detection, and quantization tasks
  • Executes Argo DAGs that declaratively express the training workflow pipeline
• The Raspberry Pi cluster hosts platform services that:
  • Provides a browser-based Argo MLOps dashboard
  • Runs data ingest jobs that subscribe to sensor data topics from the Kafka broker
  • Provides a private docker registry server
  • Hosts a K3S server
  • Hosts Argo workflows server
  • Provides a MQTT-Kafka protocol bridge
  • Hosts an embedded MQTT broker service
  • Provides ML model download OTA service
  • Hosts model registry, device registry, and training datastore services
  • Hosts Longhorn services

Here are the steps to configure this tier.

Raspberry Pi configuration

1. Download and flash the device with the “Debian Buster with Raspberry Pi” 64-bit ARM image.

2. SSH into the device and confirm the OS is 64bit ARM by running

   dpkg --print-architecture

3. Update the OS using

   sudo apt-get update
   sudo apt-get upgrade

4. Add the following lines to /boot/cmdline.txt (This is required for K3S and containerd to work correctly)

   add cgroup_enable=cpuset cgroup_enable=memory cgroup_memory=1

5. Change the hostname of each of the Raspberry Pis agentnode-raspi1 and agentnode-raspi2.
6. Reboot the device.

NVIDIA Jetson Nano configuration

1. SSH into the device and remove docker using the following commands

   dpkg -l | grep -i docker
   sudo apt-get purge -y docker-engine docker docker.io docker-ce docker-ce-cli
   sudo apt-get autoremove -y --purge docker-engine docker docker.io docker-ce
   sudo rm -rf /var/lib/docker /etc/docker
   sudo rm /etc/apparmor.d/docker
   sudo groupdel docker
   sudo rm -rf /var/run/docker.sock
   sudo rm -rf ~/.docker

2. Change the hostname of each of the Jetson Nano device agentnode-nvidia-jetson.
3. Reboot the device

At this point, the edge devices have all the prerequisite firmware and OS configurations needed to install and run the platform services. We will now install and configure various platform services for MLOps, communication, and container orchestration.

Container orchestration engine - K3S setup

In this reference infrastructure, K3S is set up in a single-server node configuration with an embedded SQLite database and requires two separate steps.

Step 1 - server node

The first step is to install and run the K3S server on the platform tier (a Raspberry Pi4 device or an equivalent VM). Here are the steps:

• Open Firewall ports 6443, 32199, 1883, and 5000 if using a cloud VM.
• Make sure the hostname resolves to the IP Address.
• Get the IP Address of the device or the external IP address of the VM (if using a VM)

• Install and run the server control node

  #replace the <IP Address> with the IP Address of the device or VM
  curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="--write-kubeconfig ~/.kube/config --write-kubeconfig-mode 666 --tls-san <IP Address> --node-external-ip=<IP Address>" sh -

• Confirm proper setup by using crictl

  crictl info

• Get the token to authorize the agent nodes

  cat /var/lib/rancher/k3s/server/token

The agent nodes get installed on all the tiers except the things tier. Install the K3S agent on the Jetson Nano and Coral TPU Dev Kits, and then confirm proper setup using crictl

#replace the <IP Address> with the IP Address of the K3S server node
#replace the <TOKEN> with the token from the server node
curl -sfL https://get.k3s.io | K3S_URL=https://<IP Address>:6443 K3S_TOKEN=<TOKEN> sh -
crictl info

With each successful agent node setup, you should be able to see the entire cluster by running this command on the K3S server node

kubectl get nodes -o wide -w

This is what I see on my cluster

Install longhorn by following these steps:

1. On the platform tier (a Raspberry Pi4 device or an equivalent VM) install longhorn by following these instructions

2. Create a new namespace architectsguide2aiot and label the raspberrypi device 1

   kubectl create ns architectsguide2aiot
   kubectl label nodes agentnode-raspi1 controlnode=active

3. Add a node selector in the longhorn.yaml file to run the following longhorn CRDs only on devices labeled controlnode=active

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: longhorn-default-setting
     namespace: longhorn-system
   data:
     default-setting.yaml: |-
       backup-target:
       backup-target-credential-secret:
       system-managed-components-node-selector:"controlnode: active"
   .
   . 
   . 
   # add this for each of the the following CRDs
   # DaemonSet/longhorn-manager
   # Service/longhorn-ui
   # Deploymentlonghorn-driver-deployer
   
   nodeSelector:
     controlnode: active

4. Install the ingress controller by following these instructions

   apiVersion: networking.k8s.io/v1
   kind: Ingress
   metadata:
     name: longhorn-ingress
     namespace: longhorn-system
     annotations:
       # type of authentication
       nginx.ingress.kubernetes.io/auth-type: basic
       # prevent the controller from redirecting (308) to HTTPS
       nginx.ingress.kubernetes.io/ssl-redirect: "false"
       # name of the secret that contains the user/password definitions
       nginx.ingress.kubernetes.io/auth-secret: basic-auth
       # message to display with an appropriate context why the authentication is required
       nginx.ingress.kubernetes.io/auth-realm: "Authentication Required "
   spec:
     rules:
       - http:
           paths:
             - pathType: Prefix
               path: "/"
               backend:
                 service:
                   name: longhorn-frontend
                   port:
                     number: 80

5. Open the longhorn dashboard and navigate to settings->general. Set the configuration to the following settings and save.

   - Replica Node Level Soft Anti-Affinity : true
   - Replica Zone Level Soft Anti-Affinity : true
   - System Managed Components Node Selector : controlnode: active

6. Label the raspberry pi device 2

   kubectl label nodes agentnode-raspi2 controlnode=active

7. Wait till all the CSI drivers and plugins are deployed and running on the raspberry pi device 2

   NAME                                       READY   STATUS    RESTARTS      AGE    IP            NODE                        NOMINATED NODE   READINESS GATES
   longhorn-csi-plugin-rw5qv                  2/2     Running   4 (18h ago)   10d    10.42.5.50    agentnode-raspi2            <none>           <none>
   longhorn-manager-dtbp5                     1/1     Running   2 (18h ago)   10d    10.42.5.48    agentnode-raspi2            <none>           <none>
   instance-manager-e-f74eeb54                1/1     Running   0             172m   10.42.5.53    agentnode-raspi2            <none>           <none>
   engine-image-ei-4dbdb778-jbw5g             1/1     Running   2 (18h ago)   10d    10.42.5.52    agentnode-raspi2            <none>           <none>
   instance-manager-r-9f692f5b                1/1     Running   0             171m   10.42.5.54    agentnode-raspi2            <none>           <none>

8. On the dashboard confirm that you see two active nodes

9. Open the volumes panel and then create a new volume with the following settings

   Name      : artifacts-registry-volm
   Size: 1 Gi 
   Replicas: 1
   Frontend  : Block Device

10. Attach this volume to the agentnode-raspi2 device. Try attaching and detaching a few times. For some reason, it takes a few retries before the volume attaches.

11. Using the dashboard create a PV and PVC in the namespace architectsguide2aiot and name it artifacts-registry-volm 

    kubectl get pv,pvc -n architectsguide2aiot
    
    NAME                                      CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                                         STORAGECLASS      REASON   AGE
    persistentvolume/artifacts-registry-volm   1Gi        RWO            Retain           Bound    architectsguide2aiot/artifacts-registry-volm   longhorn-static            12d
    
    NAME                                           STATUS   VOLUME                   CAPACITY   ACCESS MODES   STORAGECLASS      AGE
    persistentvolumeclaim/artifacts-registry-volm   Bound    artifacts-registry-volm   1Gi        RWO            longhorn-static   12d
    

Here are the steps to install and configure a private docker registry on the platform tier:

1. Install docker a Raspberry Pi4 device or an equivalent VM

   sudo apt-get update
   	sudo apt-get remove docker docker-engine docker.io
   	sudo apt install docker.io
   	sudo systemctl start docker
   	sudo systemctl enable docker

2. Now start the docker distribution service on the device or VM. This is the local docker registry. The -d flag will run it in a detached mode.

   -d -p 5000:5000 --restart=always --name registry registry:2

3. Edit /etc/docker/daemon.yaml to add an insecure registry entry

   {
   	"insecure-registries": ["localhost:5000"]
   }

Note: I highly recommend that you use a secure registry using a proper CA and signed certs by following these instructions. But, for this reference infrastructure, I am taking a shortcut and configuring an insecure registry.

4. Restart the docker service

   systemctl restart docker.service

K3S - mirror endpoints

1. Configure a mirror endpoint in the K3S server node by editing the /etc/rancher/k3s/registries.yaml

   #replace the <IP Address> with the IP Address of the node hosting the docker registry service
   mirrors:
     docker.<IP Address>.nip.io:5000:
       endpoint:
         - "http://docker.<IP Address>.nip.io:5000"
   

2. On each agent, node edit the containerd config file to add the private container registry mirror by following these steps:

• Go to the folder /var/lib/rancher/k3s/agent/etc/containerd
• In this folder make a copy of the config.toml file and name it config.toml.tmpl
• Add this section to config.toml.tmpl file

• Replace the <IP Address> with the IP Address of the node hosting the docker registry service

  #replace the <IP Address> with the IP Address of the node hosting the docker registry service
  [plugins.cri.registry]
   [plugins.cri.registry.mirrors]
    [plugins.cri.registry.mirrors."docker.io"]
      endpoint = ["https://registry-1.docker.io"]
    [plugins.cri.registry.mirrors."docker.<IP Address>.nip.io:5000"]
      endpoint = ["http://docker.<IP Address>.nip.io:5000"]

3. Restart the k3s-agent service and verify the proper configuration of the k3s-agent service using crictl

   systemctl restart k3s-agent.service
   crictl info

Docker buildx

We also need to set up docker buildx which is used to build the ARM64 compatible inference modules images. On the device hosting the docker registry, initialize and setup docker buildx

docker buildx
docker buildx create --name mybuilder 

Container Workflow Engine - Argo workflows setup

Argo workflow is used in this reference infrastructure to run parallel ML jobs expressed as DAGs.   Here are the installation and configuration steps:

1. Deploy the Argo workflow CRDs

   kubectl create ns architectsguide2aiot
   kubectl apply -n architectsguide2aiot -f https://github.com/argoproj/argo-workflows/releases/download/v3.1.11/install.yaml

2. Switch the workflow executor to the Kubernetes API. A workflow executor is a process that conforms to a specific interface that allows Argo to perform certain actions like monitoring pod logs, collecting artifacts, managing container lifecycles, etc

   kubectl patch configmap/workflow-controller-configmap \
   -n architectsguide2aiot \
   --type merge \
   -p '{"data":{"containerRuntimeExecutor":"k8sapi"}}'

3. Port forward to open the argo console in a browser

   kubectl -n architectsguide2aiot port-forward svc/argo-server 2746:2746

4. Get the auth token

   kubectl -n architectsguide2aiot exec argo-server-<pod name> -- argo auth token

5. Open the Argo console in your browser and use the auth token from the previous ste

Strimzi provides the images and operators to run and manage Kafka on a Kubernetes cluster. We will now install and configure Strimzi on one of the Raspberry Pi devices. This deployment includes the following components

• Kafka - cluster of broker nodes
• Kafka Connect - cluster for external data connections
• Kafka MirrorMaker - cluster to mirror the Kafka cluster in a secondary cluster
• Kafka Bridge - make HTTP-based requests to the Kafka cluster
• ZooKeeper - cluster of replicated ZooKeeper instances

This deployment also includes the following Strimzi Operators:

• Cluster Operator
• Entity Operator
• Topic Operator
• User Operator

Here are the Installation steps:

1. Create a namespace for strimzi deployment

   kubectl create ns architectsguide2aiot

2. Apply the Strimzi install file and then provision the Kafka Cluster

   kubectl create -f 'https://strimzi.io/install/latest?namespace=architectsguide2aiot' -n architectsguide2aiot
   kubectl apply -f 'https://strimzi.io/examples/latest/kafka/kafka-persistent-single.yaml' -n architectsguide2aiot

3. Modify the kafka-persistent-single.yaml to start the node port external listeners

   apiVersion: kafka.strimzi.io/v1beta2
   kind: Kafka
   metadata:
     name: architectsguide2aiot-aiotops-cluster
   spec:
     kafka:
       version: 2.8.0
       replicas: 1
       listeners:
         - name: plain
           port: 9092
           type: internal
           tls: false
         - name: tls
           port: 9093
           type: internal
           tls: true
         - name: external
           port: 9094
           type: nodeport
           tls: false
           configuration:
             bootstrap:
               nodePort: 32199
             brokers:
               - broker: 0
                 nodePort: 32000
               - broker: 1
                 nodePort: 32001
               - broker: 2
                 nodePort: 32002
       config:
         offsets.topic.replication.factor: 1
         transaction.state.log.replication.factor: 1
         transaction.state.log.min.isr: 1

4. Modify the tolerations and affinities to limit scheduling of pods to specific nodes

   template:
     pod:
       tolerations:
         - key: "dedicated"
           operator: "Equal"
           value: "Kafka"
           effect: "NoSchedule"
       affinity:
         nodeAffinity:
           requiredDuringSchedulingIgnoredDuringExecution:
             nodeSelectorTerms:
               - matchExpressions:
                   - key: dedicated
                     operator: In
                     values:
                       - Kafka

5. Apply the modified configuration and wait for all the services to start

   kubectl apply -f 'https://strimzi.io/examples/latest/kafka/kafka-persistent-single.yaml' -n architectsguide2aiot
   kubectl wait kafka/my-cluster --for=condition=Ready --timeout=300s -n architectsguide2aiot

See the Lightweight Pub/Sub broker section in the next post.

See the protocol bridge section in the next post.

AI acceleration - taints and labels

The devices with AI accelerators such as GPUs or TPUs need to be labeled so as the ensure placement of ML workloads on the proper AI accelerated device.

kubectl label nodes agentnode-coral-tpu1 tpuAccelerator=true
kubectl label nodes agentnode-coral-tpu2 tpuAccelerator=true
kubectl label nodes agentnode-coral-tpu3 tpuAccelerator=true
kubectl label nodes agentnode-nvidia-jetson gpuAccelerator=true

To prevent Strimzi from scheduling workloads on the devices in the inference tier use the following taints:

kubectl taint nodes agentnode-coral-tpu1 dedicated=Kafka:NoSchedule
kubectl taint nodes agentnode-coral-tpu2 dedicated=Kafka:NoSchedule
kubectl taint nodes agentnode-coral-tpu3 dedicated=Kafka:NoSchedule

Summary: Establishing a reference infrastructure on edge devices

In this post, we followed a detailed step-by-step guide for establishing a reference infrastructure on edge devices by installing and configuring various CNCF projects such as Argo, K3S, Strimzi, Longhorn, and various custom services. 

In the concluding section of the Architect's Guide to the AIoT series, we will see how to build, deploy and manage a “real world” AIoT reference application using TensorFlow Lite and TFLM and deploy it on this infrastructure.