Cloud Setup

How to set up a cluster of test machines in the cloud for load generation.


XLT enables you to develop and run test scenarios from your own machine. However, load testing often requires a lot of virtual users executing the test scenarios at the same time. Therefore we recommend to use a cluster of test machines which can deliver more hardware and more bandwidth than a single machine. XLT comes with tools to set up your own cluster using Google Cloud or Amazon Web Services.

This page contains an overview of how to use them, which caveats you might face, and enhances the general understanding of testing from the cloud.

Running Load Tests Using a Distributed Environment

A possible setup which you could use to run a load test using a distributed load generation environment looks like this:

Distributed Load Generation Environment

One machine runs your master controller, which controls several machines that run the agent controllers and agents.

XLT Images

Xceptance provides ready-to-use public XLT images for AWS machines (AMIs) and Docker. These images are based on Debian, include the latest XLT and JDK (see release notes for current version info) and are tuned for load testing. For real browser load testing, they also include the latest Chromium and Firefox (ESR) browsers with matching web driver binaries, chromedriver and geckodriver.

If you are looking for XLT images for other clouds or just want to build your own image, visit XLT’s sister project, XLT Packer. This project not only contains scripts to install and set up the contents of an XLT image, but also provides Packer templates to actually create such images for the following clouds:

  • AWS
  • DigitalOcean
  • Google
  • Hetzner

Follow the instructions in the project’s README file to create an XLT image for one of the supported clouds yourself.

Setting Up and Managing Load Test Instances

XLT ships with two small scripts that simplify the process of setting up and managing load test instances: gce_admin (for Google Cloud) and ec2_admin (for Amazon Web Services).

The scripts will set up a cluster of test machines quickly. After the setup, you just have to add the machines to the master controller configuration: navigate to <XLT>/config/ on your MC machine, then enter the AC data the tools give you when you list the created instances. Now you are ready for load testing.

After your test finished and the mastercontroller has downloaded all the data required for report generation, gce_admin/ec2_admin can also help you to easily terminate all of your test machines.

Google Cloud (GCP)

Setting up Google Cloud Access

To start the XLT gce_admin tool to manage load test machines, you need to set up GCP access first using Google’s gcloud command line tool. In order to do this, download and install the Google Cloud SDK for your platform. Follow the directions to setup the SDK.

Add the Cloud SDK command line tools to your PATH and enable command completion if you want to. In any case, restart your shell after the installation and run gcloud init to initialize the gcloud environment. This will prompt you to log in using your Google user account - log in (in your browser) and click Allow to grant permission to access Google Cloud resources.

At the command prompt, select a Google Cloud project from the list of those where you have Owner, Editor or Viewer permissions:

Pick cloud project to use:
 [1] [my-project-1]
 [2] [my-project-2]
 Please enter your numeric choice:

If you only have one project, gcloud init selects it for you. If you cannot see your project, make sure you have the appropriate permissions.

In order to create application default credentials for use by gce_admin, run:

$ gcloud auth application-default login

In the browser, log in and accept the requested permissions.

Image Templates for Google Cloud

Right now there are no public XLT gcloud images provided, however you can build your own images using our public XLT-Packer project. It contains a README on how to use it to create images for the cloud vendor of your choice.

Setting up gce_admin

Before its first usage, the gce_admin tool needs to be configured. You can do this by editing <XLT>/config/ The most important property you need to set here is the project id, which should be the same project you set earlier in gcloud:

xlt.gce.projectId = my-project-1

To use the gce_admin tool to create, list, and stop Google Cloud instances (which will run your agent controllers and agents), navigate to <XLT>/bin/ and run:

$ ./

You will see a prompt like this:

What do you want to do?
 (l) List running instances
 (c) Create managed instance groups
 (d) Delete managed instance groups
 (q) Quit

Creating GCP Instances

When starting GCP instances (c), you first have to select one or more regions to run the machines in. To select multiple regions at once, just separate your input by space, comma, or semicolon:

Select one or more regions:
  (0) <all>
  (1) Asia Pacific  - Taiwan         (asia-east1)
  (2) Asia Pacific  - Hong Kong      (asia-east2)
  (3) Asia Pacific  - Tokyo          (asia-northeast1)
  (4) Asia Pacific  - Osaka          (asia-northeast2)
  (5) Asia Pacific  - Seoul          (asia-northeast3)
  (6) Asia Pacific  - Mumbai         (asia-south1)
  (7) Asia Pacific  - Singapore      (asia-southeast1)
  (8) Asia Pacific  - Jakarta        (asia-southeast2)
  (9) Asia Pacific  - Sydney         (australia-southeast1)
 (10) <unknown>     - <unknown>      (europe-central2)
 (11) Europe        - Finland        (europe-north1)
 (12) Europe        - Belgium        (europe-west1)
 (13) Europe        - London         (europe-west2)
 (14) Europe        - Frankfurt      (europe-west3)
 (15) Europe        - Netherlands    (europe-west4)
 (16) Europe        - Zurich         (europe-west6)
 (17) Canada        - Montréal       (northamerica-northeast1)
 (18) South America - Sao Paulo      (southamerica-east1)
 (19) US            - Iowa           (us-central1)
 (20) US            - South Carolina (us-east1)
 (21) US            - North Virginia (us-east4)
 (22) US            - Oregon         (us-west1)
 (23) US            - California     (us-west2)
 (24) US            - Utah           (us-west3)
 (25) US            - Nevada         (us-west4)
=> 19 20

After you specified the region(s), choose the template to use from the list of available GCP image templates (see above for how to set up images) by entering its id. You will be prompted to enter the name of the instance group, which must only contain lowercase letters and dashes.

Enter the name of the instance group => my-test

Finally, you will be prompted to enter the number of instances to start. The number of instances will be distributed as evenly as possible across the regions you selected before, e.g. if you selected two regions and entered 8 as instance count, gce_admin will create four instances per region.

gce_admin will summarize the selected options and parameters for verification before starting the instances:

Regions             : us-central1, us-east1
Instance group name : my-test
Instance count      : 8
Instance template     
    - Name           : xlt-5-3-0--xl
    - Image          : projects/my-project1/global/images/xlt-5-3-0-v20210221
    - Machine type   : custom-16-30720

Do you want to create a managed instance group with the above configuration? [y/n] => 

Listing Running GCP instances

To get a list of running GCP instances (l), you have to select one or more regions first. To select multiple regions, just separate them by space, comma, or semicolon. You will then be prompted to select a filter to further refine the selection of machines to be displayed.

Filter instances by:
 (0) (No filter)
 (l) Name label
 (g) Instance group

Select g and gce_admin will search for instance groups. If instance groups were found, you will be prompted to pick one, multiple, or all instance groups. gce_admin will then show you the machine list in a ready to use property format.

--- Master controller configuration ---
com.xceptance.xlt.mastercontroller.agentcontrollers.ac001_us-central1-b.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac002_us-central1-a.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac003_us-central1-c.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac004_us-central1-d.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac005_us-east1-b.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac006_us-east1-d.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac007_us-east1-c.url =
com.xceptance.xlt.mastercontroller.agentcontrollers.ac008_us-east1-a.url =

You can just copy and paste the output and add it to your file to configure the machines to use for load testing. Make sure that you remove or overwrite machines from previous tests. If you want to add more machines to an existing list, make sure that the property names differ.

Terminating GCP Instances

To terminate GCP instances (d), you have to choose one or more regions for which to delete machines. gce_admin will then search for managed instance groups in these regions and prompt you for a selection. It will summarize the selected options before actually deleting any instances:

Retrieving all managed instance groups in region 'us-central1' ... OK

Select one or more instance groups:
 (0) <all>
 (1) my-project-ac-us-central1
=> 1

You selected to terminate *all* managed instances of group 'my-project-ac-us-central1'
in region: us-central1 ... 

4 running and 0 pending instance(s) found.
           Name                |      Host     |      Type       |  State  | Launch Time (UTC) | Uptime (h:mm)
my-project-ac-us-central1-6ncn | | custom-16-30720 | RUNNING |  2021-01-01 01:01 |         02:51
my-project-ac-us-central1-78rr | | custom-16-30720 | RUNNING |  2021-01-01 01:01 |         02:51
my-project-ac-us-central1-7ph9 | | custom-16-30720 | RUNNING |  2021-01-01 01:01 |         02:51
my-project-ac-us-central1-8sf2 | | custom-16-30720 | RUNNING |  2021-01-01 01:01 |         02:51

Are you sure? [y/n] =>

Amazon Web Services (AWS)

The Amazon Elastic Compute Cloud service is another perfect fit for on-demand load testing. XLT tests can easily be run from EC2 instances. But without a helpful tool, starting and stopping multiple machines as well configuring the mastercontroller properties is a lot of work. XLT comes with a simple command line tool that simplifies the handling of Amazon EC2 instances: the ec2_admin. It provides the following functionality:

  • start new instances
  • stop running instances
  • list running instances (and print a corresponding agent controller configuration ready to be pasted into

Note that this tool is not intended to replace the AWS console or similar tools.

AMIs for AWS

Xceptance provides ready to go AMIs (Amazon Machine Images) with XLT. These AMIs are pre-packaged systems, which are optimized for load testing and have XLT already installed. Using one of these AMIs may save you the work to create and maintain your own AMIs, but may also impose additional costs. Amazon will charge you for the infrastructure usage. Make sure that your security group permits communication on port 8500. This is the XLT agent port on these machines. A list of current AWS AMI ids can be found next to the release information on Github.

In addition to that, you can also build your own images using our public XLT-Packer project. It contains a README on how to use it to create images set up for running XLT for the cloud vendor of your choice.

Setting up ec2_admin

Before you can use the tool, you have to configure it appropriately. There is a configuration file for this: <xlt>/config/

The most important settings are your AWS credentials. These are needed to authorize AWS operation, which are executed on your behalf.

In addition to this, it is possible to configure the names of the AWS key pair used for each region in the properties, but if there is none defined, the tool will also prompt you for a key pair to use during instance setup.

You may also configure a proxy if one is required to be used in your environment.

## Your AWS credentials.  
aws.accessKey = <enter your access key>  
aws.secretKey = <enter your secret key>

## The AWS key pair names (as listed in AWS console / EC2 / KeyPairs) = = key-us-east-1

## The protocol to use (defaults to https).  
#aws.protocol = https

## HTTP proxy settings. = localhost  
#aws.proxy.port = 8888  
#aws.proxy.userName =  
#aws.proxy.password =

To run the tool, call one of the two scripts <xlt>/bin/ or <xlt>\bin\ec2_admin.cmd depending on your OS choice. The tool will guide you through the available operations via a menu-based UI. Keep in mind that you can easily run XLT in a heterogeneous environment and control Linux based agents from a Microsoft Windows environment.

Running AWS Instances

When starting new AWS machine instances via ec2_admin (r), you first have to choose a region and an availability zone for this region (optional).

The ec2_admin checks if there are multiple VPCs/subnets in the target region/availability zone and, if so, prompts the user to select the desired one. Otherwise it will select the default sub-net. Depending on your AWS configuration, you might not need to choose a VPC/subnet at all.

After that, you have to choose the machine image (AMI) to use from the list of available AMIs. In this list, ec2_admin will display the AMI’s name tag (or, if no name tag is present, falls back to its description).

You will then be prompted to pick an instance type. The list contains all necessary information, including available memory and expected costs:

Select the instance type to use for the new EC2 instances:
  (1) m5.large    -   2 core(s),        10 compute units,     8 GB RAM,       EBS Only, $0.096/h
  (2) m5.xlarge   -   4 core(s),        15 compute units,    16 GB RAM,       EBS Only, $0.192/h
  (3) m5.2xlarge  -   8 core(s),        31 compute units,    32 GB RAM,       EBS Only, $0.384/h
  (4) m5.4xlarge  -  16 core(s),        61 compute units,    64 GB RAM,       EBS Only, $0.768/h
  (5) m4.large    -   2 core(s),       6.5 compute units,     8 GB RAM,       EBS Only, $0.1/h
  (6) m4.xlarge   -   4 core(s),        13 compute units,    16 GB RAM,       EBS Only, $0.2/h
  (7) m4.2xlarge  -   8 core(s),        26 compute units,    32 GB RAM,       EBS Only, $0.4/h
  (8) m4.4xlarge  -  16 core(s),      53.5 compute units,    64 GB RAM,       EBS Only, $0.8/h
  (9) c5.large    -   2 core(s),         8 compute units,     4 GB RAM,       EBS Only, $0.085/h
 (10) c5.xlarge   -   4 core(s),        16 compute units,     8 GB RAM,       EBS Only, $0.17/h
 (11) c5.2xlarge  -   8 core(s),        31 compute units,    16 GB RAM,       EBS Only, $0.34/h
 (12) c5.4xlarge  -  16 core(s),        62 compute units,    32 GB RAM,       EBS Only, $0.68/h
 (13) c5.9xlarge  -  36 core(s),       139 compute units,    72 GB RAM,       EBS Only, $1.53/h
 (14) c5.18xlarge -  72 core(s),       278 compute units,   144 GB RAM,       EBS Only, $3.06/h
 (15) c4.large    -   2 core(s),         8 compute units,  3.75 GB RAM,       EBS Only, $0.1/h
 (16) c4.xlarge   -   4 core(s),        16 compute units,   7.5 GB RAM,       EBS Only, $0.199/h
 (17) c4.2xlarge  -   8 core(s),        31 compute units,    15 GB RAM,       EBS Only, $0.398/h
 (18) c4.4xlarge  -  16 core(s),        62 compute units,    30 GB RAM,       EBS Only, $0.796/h
 (19) c4.8xlarge  -  36 core(s),       132 compute units,    60 GB RAM,       EBS Only, $1.591/h
 (20) r4.large    -   2 core(s),         7 compute units, 15.25 GB RAM,       EBS Only, $0.133/h
 (21) r4.xlarge   -   4 core(s),      13.5 compute units,  30.5 GB RAM,       EBS Only, $0.266/h
 (22) r4.2xlarge  -   8 core(s),        27 compute units,    61 GB RAM,       EBS Only, $0.532/h
 (23) r4.4xlarge  -  16 core(s),        53 compute units,   122 GB RAM,       EBS Only, $1.064/h

ec2_admin will ask you how many instances you want to start, and lets you set an instance name. All instances set up in this action will be tagged with this name, so you can easily filter them later when listing or terminating instances. Keep in mind that AWS tag value restrictions apply.

Enter the number of instances to start: => 2

Enter the instance name: => myTestInstance-us-east-1

Communication between master controller and agent controllers will be encrypted in any case, but for additional security you can set your own agent controller password in the next step. Make sure you update the password in the mastercontroller properties when later using these started machines. If you leave the value empty, an XLT standard password is used.

In the next step, you can also enter host data (optional). All data entered here is appended to the /etc/hosts file on the load generator machines to allow custom DNS setups. This is not required for most test setups.

Enter agent controller password => 

Enter host data (mark line break with '\n') => 

In order to be able to log on to an EC2 machine using SSH, a key pair must have been assigned to the machine during startup. ec2_admin provides this feature when starting machines. The key pair to use for a certain AWS region can be configured in the properties, but can also be specified on the command line (-k, --key <key-pair name>. If none is set yet, ec2_admin will prompt you to pick one.

Select the key-pair to use for the new EC2 instances:
 (1) <none>
 (2) my-key-eu-central-1
 (3) my-key-us-east-1

ec2_admin then summarizes the chosen options for verification before actually starting the instances:

  AMI               : ami-01f30091020d1934a - XLT 5.4.0
  Region            : us-east-1  
  Availability zone : <unspecified>  
  VPC               : vpc-91079bf4
  Subnet            : subnet-118eea2b (us-east-1a) []
  Type              : c4.2xlarge 
  Count             : 2  
  Name              : myTestInstance-us-east-1
  Key-pair          : my-key-us-east-1  
  Password          : <none>
  Host data         : 

Do you want to run the instance(s) with the above configuration? [y/n] =>

Pass User Data When Starting Instances

In case your AWS EC2 instances need some custom configuration data (user data, in Amazon speak), e.g. for setting a custom agent controller password or adding content to /etc/hosts, you can also specify this data during ec2_admin startup. There are three supported ways to pass the user data:

  1. Store the data to a file and pass its name as a command line argument (-hf <file> or --hostDataFile <file> for host data, -pf <file> or --passwordFile <file> for the agent controller password).
  2. Pass the data as a command line argument (-h "..." or --hostData "..." for host data, -p "..." or --password "..." for the agent controller password)).
  3. When prompted by ec2_admin, enter the data.
Is "user data" parameter still valid?

Listing Running AWS Instances

ec2_admin can list the agent controller URLs for all/selected AWS instances (l). However, instances may not always have a public host name assigned. In that case the public IP address will be used as a fallback.

You will be prompted to select a region.

Selecting EC2 Instances by Name Tag

After that, you may filter instances by one or more tags: AWS (Amazon Web Services) offers the possibility to tag EC2 resources to simplify the administration of your cloud infrastructure. As a form of meta data, tags can be used to create user-friendly names and improve coordination between multiple users.

ec2_admin lets you select your EC2 resources based on the tag name. For example, when listing running instances, you can use filtering to reduce the set of instances shown:

Filter instances by one or more tags:  
(0) <none>  
(1) Name=CustomerA  
(2) Name=CustomerB  
(3) Type=WebServer  
(4) Type=AppServer  
=> 2 4

When starting Amazon EC2 machine instances, you can also specify a name tag that will be assigned to each instance that has been started. This name tag can be used later on to filter the running instances, for example when listing or terminating them.

The tool will then output a mastercontroller configuration for the chosen set of AWS machines:

Querying all instances in region 'ap-southeast-2' ... OK.
Querying all instances in region 'us-east-1' ... OK.

2 running and 0 pending instance(s) found.

--- Master controller configuration ---
com.xceptance.xlt.mastercontroller.agentcontrollers.ac001_ap-southeast-2.url =  
com.xceptance.xlt.mastercontroller.agentcontrollers.ac002_us-east-1.url =

The generated agent controller names will also contain the region in which the respective machine is running. If your load test is driven from multiple locations in the world, this way it is much easier to tell which agent controller runs in which AWS region. It also allows later filtering using merge rules for enhanced reports.

More Details about Running Instances

If you choose menu option d instead of l, ec2_admin will give you the same filtering options as above, but as a result prints more details about running or pending machine instances, like this:

Querying *all* instances in region: eu-west-2 ... OK

1 running, 0 pending and 0 stopped instance(s) found.
Name  |     Host      |   Type   | Key-Pair Name | Security Groups | Image |  State  | Launch Time (UTC) | Uptime (h:mm)
Test1 | | t2.micro | xc-eu-west-2  | default         | Proxy | running |  2020-01-01 13:11 |    111d 02:13

Terminating AWS Instances

To terminate AWS instances by ec2_admin (t), you will be prompted to select a region and filters as above.

This way, running machine instances with the same name tag assigned can be terminated all at once, even across regions. This saves you going through each region separately. Simply specify multiple regions separated by comma when prompted.

When terminating machine instances, all instances matching your selection will be listed for review before they are actually terminated. This helps to avoid terminating the wrong instances.

Filter instances by one or more tags:
 (0) Do not filter (select all)
 (1) Name=LoadTest_1
 (2) Name=LoadTest_2
=> 1

You selected to terminate instances tagged with:  Name=LoadTest_1

  in region: eu-west-2 ... OK

    2 running and 0 pending instance(s) found.
       Name    |      Host      |    Type    |    Start Time    | Uptime (h:mm)
    LoadTest_1 | | c4.2xlarge | 2017-05-02 13:55 |          0:05
    LoadTest_1 | yy.yyy.y.yyy   | c4.2xlarge | 2017-05-02 13:55 |          0:05

Are you sure? [y/n] =>

Non-Interactive Mode

The ec2_admin tool can also be used in scripted processes. It offers a non-interactive mode where all required parameters have to be passed on to command line. This makes it possible to fully automate the starting and stopping of Amazon machines.

  • Starting machines: run <region> <instance-type> <ami-id> <instance-count> <tag>
  • Stopping machines: terminate <region> <tag>

But starting and stopping machines is only half of the story. The mastercontroller also needs to know about the freshly started agent machines so it can use them for a load test. ec2_admin lists the corresponding agent machine configuration right after the machines were started. By default, it prints this configuration to the console. Alternatively, the configuration can also be written to a file via the -o option. We recommend the latter approach since the mastercontroller can read the agent configuration directly from this file later on.

For a fully automated load test process using AWS machines, use a sequence of commands similar to this example:

$ run eu-central-1 c3.2xlarge ami-de5dcdb6 5 Posters -o  
$ -auto -report -pf  
$ terminate eu-central-1 Posters

Note how ec2_admin writes the agent machine configuration to the file which in turn is passed to the mastercontroller as input. Be aware though that it may take a while until the agent controllers are up and running. To stop the master controller from complaining too early about unreachable agent controllers, you should configure an appropriate waiting time in, one minute, for example:

com.xceptance.xlt.mastercontroller.initialResponseTimeout = 60000
Last modified March 11, 2024