Scale Cluster

Everything up to this point has been done as part of the Quick Start configuration, with all the components running in a single server (VM or physical machine). We now describe how to scale Aether to run on multiple servers, where we assume this cluster-based configuration throughout the rest of this guide. Before continuing, though, you need to remove the Quick Start configuration by typing:

$ make aether-uninstall

There are two aspects of our deployment that scale independently. One is Aether proper: a Kubernetes cluster running the set of microservices that implement SD-Core and AMP (and optionally, other edge apps). The second is gNBsim: the emulated RAN that generates traffic directed at the Aether cluster. The assumption in this section is that there are at least two servers—one for the Aether cluster and one for gNBsim—with each able to scale independently. For example, having four servers would support a 3-node Aether cluster and a 1-node workload generator. This example configuration corresponds to the following hosts.ini file:

[all]
node1 ansible_host=172.16.144.50 ansible_user=aether ansible_password=aether ansible_sudo_pass=aether
node2 ansible_host=172.16.144.71 ansible_user=aether ansible_password=aether ansible_sudo_pass=aether
node3 ansible_host=172.16.144.18 ansible_user=aether ansible_password=aether ansible_sudo_pass=aether
node4 ansible_host=172.16.144.93 ansible_user=aether ansible_password=aether ansible_sudo_pass=aether

[master_nodes]
node1

[worker_nodes]
node2
node3

[gnbsim_nodes]
node4

The first block identifies all the nodes; the second block designates which node runs the Kubernetes control plane (and where you invoke kubectl commands); the third block designates the worker nodes in the Kubernetes cluster; and the last block indicate which nodes run the gNBsim workload generator (gNBsim scales across multiple Docker containers, but these containers are not managed by Kubernetes).

Although not a requirement, this and the following sections make the simplifying assumption that you install OnRamp and invoke Make targets on the master_nodes. (In general, the Ansible client that OnRamp uses to deploy Aether need not run on one of the servers listed in hosts.ini.) Also note that having master_nodes and gnbsim_nodes contain exactly one common server (as we did previously) is what triggers Ansible to instantiate the Quick Start configuration. (In general, the node groups need not be disjoint, so for example, a single node could be part of worker_nodes and gnbsim_nodes.)

You need to modify hosts.ini to match your target deployment. Once you've done that (and assuming you deleted your earlier Quick Start configuration), you can re-execute the same set of targets you ran before:

$ make aether-k8s-install
$ make aether-5gc-install
$ make aether-amp-install
$ make aether-gnbsim-install
$ make aether-gnbsim-run

This will run the same gNBsim test case as before, but originating in a separate VM. We will return to options for scaling up the gNBsim workload in a later section, along with describing how to run physical gNBs in place of gNBsim. Note that if you are primarily interested in the latter, you can still run Aether on a single server, and then connect that node to one or more physical gNBs.

Finally, apart from being able able to run SD-Core and gNBsim on separate nodes—thereby cleanly decoupling the Core from the RAN—one question we have not yet answered is why you might want to scale the Aether cluster to multiple nodes. One answer is that you are concerned about availability, so want to introduce redundancy.

A second answer is that you want to run some other edge application, such as an IoT or AI/ML platform, on the Aether cluster. Such applications can be co-located with SD-Core, with the latter providing local breakout. For example, OpenVINO is a framework for deploying inference models to process local video streams streams, for example, detecting and counting people who enter the field of view for 5G-connected cameras. Just like SD-Core, OpenVINO is deployed as a set of Kubernetes pods.