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To build, we can use the instructions from NVIDIA K8s Device Plugin for Wind River Linux to create a custom device plugin that allows the cluster to expose the number of GPUs on NVIDIA Jetson devices
/install.sh You will get the error: Error: COMMAND FAILED: '/usr/sbin/ip6tables-restore -w -n' failed: ip6tables-restore v1.8.4 (legacy): Couldn't load match `rpfilter':No such file or directory Set the IPv6 rpfilter=no in the /etc/firewalld/firewalld.conf to fix this sed -i "s|^IPv6 rpfilter=yes|IPv6 rpfilter=no|" /etc/firewalld/firewalld.conf systemctl restart firewalld Add the ssh port 22 so we are not locked out firewall-cmd --zone=public --permanent --add-port=22/tcp firewall-cmd --reload NVML (and therefore nvidia-smi) is not currently supported on Jetson. The k8s-device-plugin does not work with Jetson and the nvidia/k8s-device-plugin container image is not available for arm64
Edge devices pose very different operational, environmental, and business challenges from those of cloud computing...In the next part of this series, we will continue to build and deploy MicroShift on ARM devices, KubeVirt and Kata Containers
Small, lightweight edge devices provide just enough local compute power for the ship to operate independently, even without connectivity or remote control
- apiGroups: - scheduling.sigs.k8s.io - scheduling.x-k8s.io resources: - podgroups Install Scheduler-plugins as secondary scheduler in the cluster We can install the scheduler plugins based on the tags using the Helm Chart. If you want to clean up the previously installed scheduler plugins, have a look at separate section later. #oc delete project scheduler-plugins # if you want to reinstall oc project default # helm chart will be installed in default namespace # Installs v0.23.10 if you checkout the v0.24.9 git clone --branch v0.24.9 https://github.com/kubernetes-sigs/scheduler-plugins.git # or # Installs v0.22.6 if you checkout the v0.23.10 # git clone --branch v0.23.10 https://github.com/kubernetes-sigs/scheduler-plugins.git mv scheduler-plugins/manifests/crds/topology.node.k8s.io noderesourcetopologies.yaml /tmp # https://github.com/kubernetes-sigs/scheduler-plugins/issues/375 cd scheduler-plugins/manifests/install/charts # It will create the scheduler-plugins namespace with the two pods helm install scheduler-plugins as-a-second-scheduler/ # You can alternatively set the specific version for the Chart and images to 0.23.10 sed -i "s/ersion: 0.*/ersion: 0.23.10/g" as-a-second-scheduler/Chart.yaml helm upgrade --install scheduler-plugins as-a-second-scheduler/ --set scheduler.image=registry.k8s.io/scheduler-plugins/kube-scheduler:v0.23.10 --set controller.image=registry.k8s.io/scheduler-plugins/controller:v0.23.10 Do not install using the images with v0.24.9, it will result in CSIStorageCapacity errors in the scheduler-plugins-scheduler pod logs because the version is at v1beta1 instead of v1 in OpenShift 4.10
The IoT Edge is placed right in between the devices layer and the IoT Central...Edge layer runs edge computing logic on the data ingested by devices while providing analytics in real-time
The following video provides a deeper dive on edge computing: How edge devices and edge servers work together Edge devices drive edge computing, and they're already widespread in use and continuing to multiply constantly. To get a sense of the magnitude, imagine you’re running a delivery company and visiting a warehouse with the employees using the following devices: Shipping equipment monitors Camera monitors Mobile phones All of these edge devices are collecting data that is accessible digitally. Edge devices are different from edge servers , which are pieces of IT equipment designed for edge computing. In our example, edge servers could sit in the warehouse and collect and process data from those edge devices. Using an edge server rather than forwarding that data to the cloud for processing and delivery back to edge devices gives you the following advantages: Process and compute data where it is created Reduce overall latency Separate responsibilities Many executives at telecommunications companies are looking closely at edge computing for these reasons
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Furthermore, there exists literally hundreds of plugins for Telegraf to monitor a wide variety of devices, services and software. A search however, didn’t reveal the existence of any plugin to monitor LSF