Optimizing a sample application running on AWS

In this example, you will go through the optimization of a Spark based PageRank algorithm on AWS instances. We’ll be using a PageRank implementation included in Renaissance, an industry-standard Java benchmarking suite developed by Oracle Labs, tweaking 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:

#!/bin/bash
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:

System

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
properties:
    prometheus:
      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
properties:
    prometheus:
      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:

metrics:
  - 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
metrics:
  - 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
properties:
  hostname: renaissance.akamas.io
  sshPort: 22
  instance: ec2_instance
  username:  ubuntu
  key: # SSH KEY
  ec2:
    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

Workflow

The workflow in this example is composed by three main steps:

  1. Update the instance type

  2. Run the application benchmark

  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. Update the instance size

    1. Generate the the playbook file from the template

    2. Update the instance using the playbook

    3. Wait for the instance to be available

  2. Run the application benchmark

    1. Configure the benchmark Java launch script

    2. Execute the launch script

    3. Parse PageRank output to make it consumable by the CSV telemetry instance

  3. Stop the instance

    1. Configure the playbook to stop an instance with a specific instance id

    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
  tasks:
  - name: save instance info
    ec2_instance_info:
      filters:
        "tag:Name": <your-instance-name>
    register: ec2
  - name: Stop the instance
    ec2:
      region: <your-aws-region>
      state: stopped
      instance_ids:
        - "{{ 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
    ec2:
      region: <your-aws-region>
      state: running
      instance_ids:
        - "{{ ec2.instances[0].instance_id }}"
      wait: True
    register: ec2
  - name: wait for SSH to come up
    wait_for:
      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
tasks:

  # Creating the EC2 instance
  - name: Configure provisioning
    operator: FileConfigurator
    arguments:
      sourcePath: /home/ubuntu/ansible/resize.yaml.templ
      targetPath: /home/ubuntu/ansible/resize.yaml
      host:
        hostname: bastion.akamas.io
        username: ubuntu
        key: # SSH KEY

  - name: Execute Provisioning
    operator: Executor
    arguments:
      command: ansible-playbook /home/akamas/ansible/resize.yaml
      host:
        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
    arguments:
      seconds: 120

  # Running the benchmark
  - name: Configure Benchmark
    operator: FileConfigurator
    arguments:
        source:
            hostname: renaissance.akamas.io
            username: ubuntu
            path: /home/ubuntu/renaissance/launch_benchmark.sh.templ
            key: # SSH KEY
        target:
            hostname: renaissance.akamas.io
            username: ubuntu
            path: /home/ubuntu/renaissance/launch_benchmark.sh
            key: # SSH KEY

  - name: Launch Benchmark
    operator: Executor
    arguments:
      command: bash /home/ubuntu/renaissance/launch_benchmark.sh
      host:
        hostname: renaissance.akamas.io
        username: ubuntu
        key: # SSH KEYCreate the workflow resource by typing in your terminal:

Telemetry

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

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
global:
  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.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: prometheus
    static_configs:
    - targets: ['localhost:9090']

  - job_name: jmx
    static_configs:
    - targets: ["localhost:9110"]
    relabel_configs:
    - source_labels: ["__address__"]
      regex: "(.*):.*"
      target_label: instance
      replacement: jmx_instanc

The config.yml file you have to create in the ~/renaissance folder:

startDelaySeconds: 0
username:
password:
ssl: false
lowercaseOutputName: false
lowercaseOutputLabelNames: false
# using the property above we are telling the export to export only relevant java metrics
whitelistObjectNames:
  - "java.lang:*"
  - "jvm:*"

Now you can create a prometheus-instance.yaml file:

provider: Prometheus
config:
  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
config:
  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

metrics:
  - metric: elapsed
    datasourceMetric: nanos

Then create the resource by typing in your terminal:

akamas create telemetry-instance renaissance

Study

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

goal:
  objective: minimize
  function:
    formula: benchmark.elapsed * aws.aws_ec2_price

workflow: workflow-aws

parametersSelection:
  - 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

steps:
  - name: baseline
    type: baseline
    numberOfTrials: 2
    values:
     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

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