Packer eases the virtual machines process creation. You could check the packer web site for further information.
Now, We use it to create virtual machine images and prepare it with the contextualization script (see the of the documentation).

Checkout the snooze-packer project for the first images provided with the tool : https://github.com/snoozesoftware/snooze-packer

1 System deployment

You just have to follow the deployment procedure explained in the documentation.

After that you can launch some virtual machines. Since those virtual machines will host hadoop services we suggest you to set (at least) the number of vcpus to 3 and ram to 3GB.

2 Configure hadoop

Once deployed, you will find the hadoop deployment scripts on the first bootstrap :

$bootstrap) cd /tmp/snooze/experiments

You need to create a file containing the IPs addresses of your virtual machines.
To achieve this you can make a request to the EC2 API to get the list of instances running, this will return a XML containing all the instances (and their IPs).
Finally you will just have to parse the output to get the IPs.

In the following code, we assume that you are connected to the first bootstrap and that the SnoozeEC2 service is running on this node.


$bootstrap) curl localhost:4001?Action=DescribeInstances > instances


The following code will output the list of IPs addresses of your running virtual machines. You have to redirect this to the file /tmp/snooze/experiments/tmp/virtual_machine_hosts.txt.


require 'rexml/document'
include REXML
# instance file contains the output of "curl snoozeec2?Action=DescribeInstances"
file = File.new 'instances'
doc = Document.new file

XPath.each(doc, "//ipAddress"){ |item| puts item.text}


Finally, go to /tmp/snooze/experiments/ and launch :

$bootstrap) ./experiments -m configure
--
[Snooze-Experiments] Configuration mode (normal, variable_data):
normal


3 Launch a benchmark

You can get the list of available benchmark by typing :


$bootstrap) ./experiments -m benchmark
--
[Snooze-Experiments] Benchmark name (e.g. dfsio, dfsthroughput, mrbench, nnbench,                pi, teragen, terasort, teravalidate, censusdata, censusbench, wikidata, wikibench):



Choose one and you’re done.