Akamas Docs
Ask or search…

Optimizing a sample application running on AWS

In this example, you will go through the optimization of a Spark application running on AWS instances. We’ll be using a PageRank implementation included in Renaissance, an industry-standard Java benchmarking suite, tuning both Java and AWS parameters to improve the performance of our application.

Environment setup

For this example, you’re expected to use two dedicated machines:
  • an Akamas instance
  • a Linux-based AWS EC2 instance
The Akamas instance requires provisioning and manipulating instances, therefore it requires to be enabled to do so by setting AWS Policies, integrating with orchestration tools (such as Ansible), and an inventory linked to your AWS EC2 environment.
The Linux-based instance will run the application benchmark, so it requires the latest open-jdk11 release
sudo apt install openjdk-11-jre

Telemetry Infrastructure setup

For this study you’re going to require the following telemetry providers:

Application and Test tool

The renaissance suite provides the benchmark we’re going to optimize.
Since the application consists of a jar file only, the setup is rather straightforward; just download the binary in the ~/renaissance/ folder:
mkdir ~/renaissance
cd ~/renaissance
wget -O renaissance.jar https://github.com/renaissance-benchmarks/renaissance/releases/download/v0.10.0/renaissance-gpl-0.10.0.jar
In the same folder upload the template file launch.benchmark.sh.temp, containing the script that executes the benchmark using the provided parameters and parses the results:
java -XX:MaxRAMPercentage=60 ${jvm.*} -jar renaissance.jar -r 50 --csv renaissance.csv page-rank
total_time=$(awk -F"," '{total_time+=$2}END{print total_time}' ./renaissance.csv)
first_line=$(head -n 1 renaissance.csv)
end_time=$(tail -n 1 renaissance.csv | cut -d',' -f3)
start_time=$(sed '2q;d' renaissance.csv | cut -d',' -f4)
echo $first_line,"TS,COMPONENT" > renaissance-parsed.csv
ts=$(date -d @$(($start_time/1000)) "+%Y-%m-%d %H:%M:%S")
echo -e "page-rank,$total_time,$end_time,$start_time,$ts,pagerank" >> renaissance-parsed.csv
You may find further info about the suite and its benchmarks in the official doc.

Optimization setup

In this section, we will guide you through the steps required to set up the optimization on Akamas.

Optimization packs

This example requires the installation of the following optimization packs:


Our system could be named renaissance after its application, so you’ll have a system.yaml file like this:
name: jvm
description: The JVM running the benchmark
componentType: java-openjdk-11
job: jmx
instance: jmx_instance
Then create the new system resource:
akamas create component component-jvm.yaml renaissance
The renaissance system will then have three components:
  • A benchmark component
  • A Java component
  • An EC2 component, i.e. the underlying instance
Java component
Create a component-jvm.yaml file like the following:
name: jvm
description: The JVM running the benchmark
componentType: java-openjdk-11
job: jmx
instance: jmx_instance
Then type:
akamas create component component-jvm.yaml renaissance
Benchmark component
Since there is no optimization pack associated with this component, you have to create some extra resources.
  • A metrics.yaml file for a new metric tracking execution time:
- name: elapsed
unit: nanoseconds
description: The duration of the benchmark execution
  • A component-type benchmark.yaml:
name: benchmark
description: A component type for the Renaissance Java benchmarking suite
- name: elapsed
parameters: []
  • The component pagerank.yaml:
name: pagerank
description: The pagerank application included in Renaissance benchmarks
componentType: benchmark
Create your new resources, by typing in your terminal the following commands:
akamas create metrics metrics.yaml
akamas create component-type benchmark.yaml
akamas create component pagerank.yaml renaissance
EC2 component
Create a component-ec2.yaml file like the following:
name: instance
description: The ec2 instance the benchmark runs on
componentType: ec2
hostname: renaissance.akamas.io
sshPort: 22
instance: ec2_instance
username: ubuntu
key: # SSH KEY
region: us-east-2 # This is just a reference
Then create its resource by typing in your terminal:
akamas create component component-ec2.yaml renaissance


The workflow in this example is composed of three main steps:
  1. 1.
    Update the instance type
  2. 2.
    Run the application benchmark
  3. 3.
    Stop the instance
To manage the instance we are going to integrate a very simple Ansible in our workflow: the FileConfigurator operator will replace the parameters in the template file in order to generate the code run by the Executor operator, as explained in the Ansible page.
In detail:
  1. 1.
    Update the instance size
    1. 1.
      Generate the playbook file from the template
    2. 2.
      Update the instance using the playbook
    3. 3.
      Wait for the instance to be available
  2. 2.
    Run the application benchmark
    1. 1.
      Configure the benchmark Java launch script
    2. 2.
      Execute the launch script
    3. 3.
      Parse PageRank output to make it consumable by the CSV telemetry instance
  3. 3.
    Stop the instance
    1. 1.
      Configure the playbook to stop an instance with a specific instance id
    2. 2.
      Run the playbook to stop the instance
The following is the template of the Ansible playbook:
# Change instance type, requires AWS CLI
- name: Resize the instance
hosts: localhost
gather_facts: no
connection: local
- name: save instance info
"tag:Name": <your-instance-name>
register: ec2
- name: Stop the instance
region: <your-aws-region>
state: stopped
- "{{ ec2.instances[0].instance_id }}"
instance_type: "{{ ec2.instances[0].instance_type }}"
wait: True
- name: Change the instances ec2 type
shell: >
aws ec2 modify-instance-attribute --instance-id "{{ ec2.instances[0].instance_id }}"
--instance-type "${ec2.aws_ec2_instance_type}.${ec2.aws_ec2_instance_size}"
delegate_to: localhost
- name: restart the instance
region: <your-aws-region>
state: running
- "{{ ec2.instances[0].instance_id }}"
wait: True
register: ec2
- name: wait for SSH to come up
host: "{{ item.public_dns_name }}"
port: 22
delay: 60
timeout: 320
state: started
with_items: "{{ ec2.instances }}"
The following is the workflow configuration file:
name: Pagerank AWS optimization
# Creating the EC2 instance
- name: Configure provisioning
operator: FileConfigurator
sourcePath: /home/ubuntu/ansible/resize.yaml.templ
targetPath: /home/ubuntu/ansible/resize.yaml
hostname: bastion.akamas.io
username: ubuntu
key: # SSH KEY
- name: Execute Provisioning
operator: Executor
command: ansible-playbook /home/akamas/ansible/resize.yaml
hostname: bastion.akamas.io
username: akamas
key: # SSH KEY
# Waiting for the instance to come up and set up its DNS
- name: Pause
operator: Sleep
seconds: 120
# Running the benchmark
- name: Configure Benchmark
operator: FileConfigurator
hostname: renaissance.akamas.io
username: ubuntu
path: /home/ubuntu/renaissance/launch_benchmark.sh.templ
key: # SSH KEY
hostname: renaissance.akamas.io
username: ubuntu
path: /home/ubuntu/renaissance/launch_benchmark.sh
key: # SSH KEY
- name: Launch Benchmark
operator: Executor
command: bash /home/ubuntu/renaissance/launch_benchmark.sh
hostname: renaissance.akamas.io
username: ubuntu
key: # SSH KEYCreate the workflow resource by typing in your terminal:


If you have not installed the Prometheus telemetry provider or the CSV telemetry provider yet, take a look at the telemetry provider pages Prometheus provider and CSV Provider to proceed with the installation.
Prometheus allows us to gather jvm execution metrics through the jmx exporter: download the java agent required to gather metrics from here, then update the two following files:
  • The prometheus.yml file, located in your Prometheus folder:
# my global config
scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
- job_name: prometheus
- targets: ['localhost:9090']
- job_name: jmx
- targets: ["localhost:9110"]
- source_labels: ["__address__"]
regex: "(.*):.*"
target_label: instance
replacement: jmx_instanc
The config.yml file you have to create in the ~/renaissance folder:
startDelaySeconds: 0
ssl: false
lowercaseOutputName: false
lowercaseOutputLabelNames: false
# using the property above we are telling the export to export only relevant java metrics
- "java.lang:*"
- "jvm:*"
Now you can create a prometheus-instance.yaml file:
provider: Prometheus
address: renaissance.akamas.io
port: 9090
Then you can install the telemetry instance:
akamas create telemetry-instance prometheus-instance.yaml renaissance
You may find further info on exporting Java metrics to Prometheus here.
CSV - Telemetry instance
Create a telemetry-csv.yaml file to read the benchmark output:
provider: CSV
protocol: scp
address: renaissance.akamas.io
username: ubuntu
authType: key
auth: # SSH KEY
remoteFilePattern: /home/ubuntu/renaissance/renaissance-parsed.csv
csvFormat: horizontal
componentColumn: COMPONENT
timestampColumn: TS
timestampFormat: yyyy-MM-dd HH:mm:ss
- metric: elapsed
datasourceMetric: nanos
Then create the resource by typing in your terminal:
akamas create telemetry-instance renaissance


Here we provide a reference study for AWS. As we’ve anticipated, the goal of this study is to optimize a sample Java application, the PageRank benchmark you may find in the renaissance benchmark suite by Oracle.
Our goal is rather simple: minimizing the product between the benchmark execution time and the instance price, that is, finding the most cost-effective instance for our application.
Create a study.yaml file with the following content:
name: aws
description: Tweaking aws and the JVM to optimize the page-rank application.
system: renaissance
objective: minimize
formula: benchmark.elapsed * aws.aws_ec2_price
workflow: workflow-aws
- name: aws.aws_ec2_instance_type
categories: [c5,c5d,c5a,m5,m5d,m5a,r5,r5d,r5a]
- name: aws.aws_ec2_instance_size
categories: [large,xlarge,2xlarge,4xlarge]
- name: jvm.jvm_gcType
- name: jvm.jvm_newSize
- name: jvm.jvm_maxHeapSize
- name: jvm.jvm_minHeapSize
- name: jvm.jvm_survivorRatio
- name: jvm.jvm_maxTenuringThreshold
- name: baseline
type: baseline
numberOfTrials: 2
aws.aws_ec2_instance_type: c5
aws.aws_ec2_instance_size: 2xlarge
jvm.jvm_gcType: G1
- name: optimize
type: optimize
numberOfExperiments: 60
Then create the corresponding Akamas resource and start the study:
akamas create study study.yaml
akamas start study aws
Last modified 1mo ago