The performance of your Anbox Cloud deployment depends on multiple factors. To ensure optimal performance, check and monitor all areas and tune your deployment based on your findings.
To measure the performance based on different parameters, you should run performance benchmarks. See the provided Performance benchmarks as a reference for what performance you can expect with different hardware configurations.
The main areas for performance tuning are:
- Instance density
- CPU access for an instance
- Hardware and network setup
- Startup time for an instance
- Client devices
The most apparent performance aspect is how many instances you can run on each of your machines.
Of course, the instance density depends a lot on the available hardware. See capacity planning information to estimate the necessary capacity and the hardware requirements for your Anbox Cloud deployment.
In addition, check your applications and make sure they use the resources in a fair way. Applications should avoid spikes in GPU utilisation, because such spikes require the application to reserve more resources and therefore reduce the instance density.
Generally, applications should use the smallest suitable resource preset. However, if you see an overall bad performance when running the application, using a more powerful resource preset usually helps (even though it reduces the instance density). As an example, consider an application that runs on an Anbox Cloud deployment that does not have any GPUs installed. In this case, the rendering workload is put on the CPU instead of the GPU, and if the resource preset of the application does not have a sufficient number of vCPU cores, the performance of the application is impacted. This can show, for example, in the virtual keyboard being really slow. By switching to a more powerful resource preset, the instance density is reduced, but the performance of each application instance is increased.
AMS has different modes to grant CPU access to an instance. The
cpu.limit_mode configuration option can be used to change the mode. The possible modes are:
This mode uses the LXD
limits.cpuconfiguration option to pin a set of CPU cores to an instance. LXD is responsible for allocating a specific number of cores to an instance and load-balancing all running instances on all available cores.
pinningrequires a system with cgroup-v2 enabled. Otherwise, limitations of cgroup-v1 might cause the load distribution over available CPU cores to not be optimal. cgroup-v2 is enabled by default starting with Ubuntu 22.04 and can be enabled on Ubuntu 20.04 by booting with
systemd.unified_cgroup_hierarchy=1added to the kernel boot parameters.
By default, AMS uses the
scheduler option, because it provides the most generic solution to a large set of use cases that Anbox Cloud supports. However, in some cases CPU pinning might be the better option to distribute load across all available CPU cores on a system.
See Requirements for the minimum hardware requirements for Anbox Cloud. Note that these list the minimum requirements, and using more powerful hardware will increase performance.
For optimal performance, you should use a dedicated block device for LXD storage. Using a loop file is considerably slower. See LXD storage for more information.
The overall performance depends not only on the hardware used for the actual Anbox Cloud deployment, but also on the setup used for other components that Anbox Cloud relies on. For example, the etcd database must use a hard disk that is fast enough. See Hardware recommendations for detailed information.
Also make sure that there is a stable network connection between the nodes of your cluster, to decrease the latency between nodes.
A very noticeable performance issue is a long wait time when starting an application.
When a user starts an application, Anbox Cloud retrieves the application image and launches a new instance for it. By default, Anbox Cloud turns off image compression in LXD when launching an instance from an image. This method speeds up the launch of the instance (because the image does not need to be uncompressed), but it causes more traffic over the network (because the image is transferred uncompressed). If the network connection between your cluster nodes is rather slow, the overall instance startup time might improve by enabling image compression. You can change the default configuration by setting the images_compression_algorithm configuration on the
ams-lxd charm. Of course, in addition to compression, the size of the image is also relevant. The smaller the image, the faster it can be synchronised across the LXD nodes in a cluster.
Another configuration that affects the instance startup time is shiftfs_enabled. This configuration is currently disabled by default, because it can cause issues with some Android applications. However, if your applications run fine with
shiftfs_enabled set, it can considerably improve the instance startup time. You should be aware though that support for shiftfs might be dropped in future releases.
You should also check the hooks that you use in your application. If you use any startup hooks (
post-start) that take a long time or wait for resources to become available, the instance startup is delayed. If you use a
post-stop hook that prolongs the shutdown of an instance, this might also affect the startup time of new instances (because it might not be possible to start more instances until the existing instances terminate).
In addition to optimising the performance of your Anbox Cloud deployment, you must also make sure that the client devices that access it can fully utilise its capabilities.
All client devices that access an Anbox Cloud deployment must be capable of low-latency video decoding. They must also use a compatible version of Android WebView (version 90 at the minimum, and ideally the latest stable release).
Furthermore, the network connection is crucial. When implementing your applications, you must take into account what kind of connection the client devices will usually have (for example, 4G, 5G or WiFi), so that you can optimise the network traffic that your applications require.
Also make sure to optimise the network path from the Anbox Cloud server to the client devices. This optimisation could be very specific to your use case. For public clouds, it often means choosing the region that is located closest to the end users. When using a bare metal installation, you should deploy servers that are geographically close to the end users. There might also be other solutions depending on the network service route.