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. Minimally, two servers are
required—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
[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 node4 [gnbsim_nodes] node4
The first block identifies all the nodes; the second block designates
which node runs the Ansible client and the Kubernetes control plane
(this is the node you ssh into and invoke Make targets and
commands); the third block designates the worker nodes being managed
by the Ansible client; 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).
Note that having
gnbsim_nodes contain exactly
one/common server is what triggers Ansible to instantiate the Quick
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
$ make aether-k8s-install $ make aether-5gc-install $ aeither-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.
A third possible answer is that you want to scale SD-Core itself, in
support of a scalable number of UEs. For example, providing
predictable, low-latency support for hundreds or thousands of IoT
devices requires horizontally scaling the AMF. OnRamp provides a way
to experiment with exactly that possibility. If you edit the
vars/main.yml to use an alternative values file (in
you can deploy SD-Core with Horizontal Pod Autoscaling (HPA) enabled. Note that HPA is an experimental feature of SD-Core; it has not yet been officially released and is not yet supported.