How do you configure AWS autoscaling to scale up quickly? I've setup an AWS autoscaling group with an ELB. All is working well, except it takes several minutes before the new instances are added and are online. I came across the following in a post about Puppet and autoscaling:
The time to scale can be lowered from several minutes to a few seconds if the AMI you use for a group of nodes is already up to date.
http://puppetlabs.com/blog/rapid-scaling-with-auto-generated-amis-using-puppet/
Is this true? Can time to scale be reduced to a few seconds? Would using puppet add any performance boosts?
I also read that smaller instances start quicker than larger ones:
Small Instance 1.7 GB of memory, 1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit), 160 GB of instance storage, 32-bit platform with a base install of CentOS 5.3 AMI
Amount of time from launch of instance to availability: Between 5 and 6 minutes us-east-1c
Large Instance 7.5 GB of memory, 4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each), 850 GB of instance storage, 64-bit platform with a base install of CentOS 5.3 AMI
Amount of time from launch of instance to availability:
Between 11 and 18 minutes us-east-1cBoth were started via command line using Amazons tools.
http://www.philchen.com/2009/04/21/how-long-does-it-take-to-launch-an-amazon-ec2-instance
I note that the article is old and my c1.xlarge instances are certainly not taking 18min to launch. Nonetheless, would configuring an autoscale group with 50 micro instances (with an up scale policy of 100% capacity increase) be more efficient than one with 20 large instances? Or potentially creating two autoscale groups, one of micros for quick launch time and one of large instances to add CPU grunt a few minutes later? All else being equal, how much quicker does a t1.micro come online than a c1.xlarge?
you can increase or decrease the time of reaction for an autoscaller by playing with "--cooldown" value (in seconds). regarding the types of instances to be used, this is mostly based on the application type and a decision on this topic should be taken after close performance monitor and production tuning.