Autoscaling


Autoscaling[], also spelled auto scaling or auto-scaling, and sometimes also called automatic scaling, is a method used in cloud computing, whereby the amount of computational resources in a server farm, typically measured in terms of the number of active servers, which vary automatically based on the load on the farm. Typically this means the number of servers you pay for goes up or down as users are busy or quiet on your web servers. It is closely related to, and builds upon, the idea of load balancing.

Advantages

Autoscaling offers the following advantages:
Autoscaling differs from having a fixed daily, weekly, or yearly cycle of server use in that it is responsive to actual usage patterns, and thus reduces the potential downside of having too few or too many servers for the traffic load. For instance, if traffic is usually lower at midnight, then a static scaling solution might schedule some servers to sleep at night, but this might result in downtime on a night where people happen to use the Internet more. Autoscaling, on the other hand, can handle unexpected traffic spikes better.

Terminology

In the list below, we use the terminology used by Amazon Web Services. However, alternative names are noted and terminology that is specific to the names of Amazon services is not used for the names.
Name MeaningAlternative names
InstanceA single server or machine that is part of the group of machines subject to autoscaling
Autoscaling groupThe collection of instances subject to autoscaling, along with all the associated policies and state informationManaged instance group
SizeThe number of instances currently part of the autoscaling group
Desired capacity The number of instances that the autoscaling group should have at any given point in time. If the size is less than the desired size, the autoscaling group will try to launch new instances. If the size is more than the desired size, the autoscaling group will try to remove instances
Minimum sizeA number of instances below which the desired capacity is not allowed to fall
Maximum sizeA number of instances above which the desired capacity is not allowed to rise
MetricA measurement associated with the autoscaling group, for which a time series of data points is generated regularly. Thresholds for metrics can be used to set autoscaling policies. Metrics can be based on aggregates of metrics for instances of the autoscaling group, or based on load balancers associated with the autoscaling group
Scaling policy A policy that specifies a change to the autoscaling group's desired capacity in response to metrics crossing specific thresholds. Scaling policies can have associated cooldown periods, which prevent additional scaling actions from occurring immediately after a specific scaling action. Changes to desired capacity could be incremental or could specify a new value of the desired capacity. Policies that increase the desired capacity are called "scaling out" or "scaling up" policies, and policies that decrease the desired capacity are called "scaling in" or "scaling down" policies
Health checkA way for the autoscaling group to determine if the instances attached to it are functioning properly. A health check may be based on whether the instance still exists and is reachable, or it could be based on whether the instance is still registered and in service with an associated load balancer
Launch configurationA description of the parameters and scripts used when launching a new instance. This includes the instance type, purchase options, possible availability zones for launch, machine image, and scripts to run on launchInstance template
Manual scalingA scaling action executed manually
Scheduled scalingA scaling policy that is executed at a specific time, for instance, time of day or week or month or year. See #Scheduled scaling for more

Practice

Amazon Web Services (AWS)

Amazon Web Services launched the Amazon Elastic Compute Cloud service in August 2006, that allowed developers to programmatically create and terminate instances. At the time of initial launch, AWS did not offer autoscaling, but the ability to programmatically create and terminate instances gave developers the flexibility to write their own code for autoscaling.
Third-party autoscaling software for AWS began appearing around April 2008. These included tools by Scalr and RightScale. RightScale was used by Animoto, which was able to handle Facebook traffic by adopting autoscaling.
On May 18, 2009, Amazon launched its own autoscaling feature along with Elastic Load Balancing, as part of Amazon Elastic Compute Cloud. Autoscaling is now an integral component of Amazon's EC2 offering. Autoscaling on Amazon Web Services is done through a web browser or the command line tool.. On May 2016 Autoscaling was also offered in AWS ECS Service.
On-demand video provider Netflix documented their use of autoscaling with Amazon Web Services to meet their highly variable consumer needs. They found that aggressive scaling up and delayed and cautious scaling down served their goals of uptime and responsiveness best.
In an article for TechCrunch, Zev Laderman, the co-founder and CEO of Newvem, a service that helps optimize AWS cloud infrastructure, recommended that startups use autoscaling in order to keep their Amazon Web Services costs low.
Various best practice guides for AWS use suggest using its autoscaling feature even in cases where the load is not variable. That is because autoscaling offers two other advantages: automatic replacement of any instances that become unhealthy for any reason, and automatic replacement of spot instances that get interrupted for price or capacity reasons, making it more feasible to use spot instances for production purposes. Netflix's internal best practices require every instance to be in an autoscaling group, and its conformity monkey terminates any instance not in an autoscaling group in order to enforce this best practice.

Microsoft's Windows Azure

On June 27, 2013, Microsoft announced that it was adding autoscaling support to its Windows Azure cloud computing platform. Documentation for the feature is available on the Microsoft Developer Network.

Oracle Cloud

allows server instances to automatically scale a cluster in or out by defining an auto-scaling rule. These rules are based on CPU and/or memory utilization and determine when to add or remove nodes.

Google Cloud Platform

On November 17, 2014, the Google Compute Engine announced a public beta of its autoscaling feature for use in Google Cloud Platform applications. As of March 2015, the autoscaling tool is still in Beta.

Facebook

In a blog post in August 2014, a Facebook engineer disclosed that the company had started using autoscaling to bring down its energy costs. The blog post reported a 27% decline in energy use for low traffic hours and a 10-15% decline in energy use over the typical 24-hour cycle.

Kubernetes Horizontal Pod Autoscaler

Horizontal Pod Autoscaler automatically scales the number of pods in a , or based on observed CPU utilization

Alternative autoscaling decision approaches

Autoscaling by default uses reactive decision approach for dealing with traffic scaling: scaling only happens in response to real-time changes in metrics. In some cases, particularly when the changes occur very quickly, this reactive approach to scaling is insufficient. Two other kinds of autoscaling decision approaches are described below.

Scheduled autoscaling approach

This is an approach to autoscaling where changes are made to the minimum size, maximum size, or desired capacity of the autoscaling group at specific times of day. Scheduled scaling is useful, for instance, if there is a known traffic load increase or decrease at specific times of the day, but the change is too sudden for reactive approach based autoscaling to respond fast enough. AWS autoscaling groups support scheduled scaling.

Predictive autoscaling

This approach to autoscaling uses predictive analytics. The idea is to combine recent usage trends with historical usage data as well as other kinds of data to predict usage in the future, and autoscale based on these predictions.
For parts of their infrastructure and specific workloads, Netflix found that Scryer, their predictive analytics engine, gave better results than Amazon's reactive autoscaling approach. In particular, it was better for:
On November 20, 2018, AWS announced that predictive scaling would be available as part of its autoscaling offering.