»Nomad Autoscaler Overview
This section details the Nomad Autoscaler, a horizontal application and cluster autoscaler for Nomad. The Nomad Autoscaler is built and released separately to Nomad. It runs as a daemon process, often referred to as the Autoscaler Agent. An Autoscaler Agent is a separate and distinct process from a Nomad Agent. The source code can be viewed on GitHub and releases are available on the HashiCorp releases page or via Docker Hub.
The Nomad Autoscaler repository includes a number of demos which provide guided learning on running the Nomad AutoScaler. Since Autoscaler Agent runs as its own daemon, it requires its own configuration. Configuration can be passed as command line flags or parsed from a configuration file. The demos repository includes an example config file that is useful to reference when configuring your Autoscaler Agent.
Horizontal application autoscaling is the process of automatically controlling the
number of instances of an application to have sufficient work throughput to meet
service-level agreements (SLA). In Nomad, horizontal application autoscaling can
be achieved by modifying the number of allocations in a task group based on the
value of a relevant metric, such as CPU and memory utilization or number of open
connections. This is enabled by configuring autoscaling policies
on individual Nomad jobs using the
Horizontal cluster autoscaling is the process of adding or removing Nomad clients from a cluster to ensure there is an appropriate amount of cluster resource for the scheduled applications. This is achieved by interacting with remote providers to start or terminate new Nomad clients based on metrics such as the remaining free schedulable CPU or memory. Cluster scaling is enabled by configuring the autoscaler agent with policies targeting the Nomad cluster.
This functionality only exists in Nomad Autoscaler Enterprise. This is not present in the open source version of Nomad Autoscaler.
Dynamic Application Sizing enables organizations to optimize the resource consumption of applications using sizing recommendations from Nomad. It evaluates, processes and stores historical task resource usage data, making recommendations for CPU and Memory resource parameters. The recommendations can be calculated using a number of different algorithms to ensure the recommendation best fits the application profile.