Kubernetes add many enhancements and feature sets for edge-based network infrastructure.
Streamlines workloads and resource management using policy based scheduling.
Adds security and networking features.
Enables auto-scaling and traffic shaping for better resource utilization and workload prioritization.
Apart from Kubernetes Edge IoT working group community, there are key developments are in progress by many companies to integrate and utilize Kubernetes power for edge and IoT. I will cover more details in upcoming articles about Kubernetes for edge.
The technology world is looking for flexible IT infrastructure that will easily evolve to meet changing data and performance requirements in support of the onslaught of upcoming and lucrative use cases. Kmesh addresses data management and data sovereignty concerns while decreasing costs associated with storage and network resources.
This article talks about how Kubernetes has emerged from container orchestration platform to manage complex workloads in AI and Machine Learning Stacks, Managing containers in NFV architecture and handling hardware GPU resources.
Kubernetes works on the principle of assigning IP addresses to pods, called as “IP-per-pod” model. The IPAM (IP address management) task is left to third party solutions. Some of these solutions include Docker networking, Flannel, IPvlan, contive, OpenVswitch, GCE and others.
The Kubernetes architecture consists of master node, replication controller in addition (or conjunction) to nodes used to host the pods. Before we go ahead, here is a review of Kubernetes terms.