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Using AWS to get the data

As described in the paper, our results were produced using r5.2xlarge Amazon EC2 instances. We used dedicated instances for stability.

We ran 200 instances simultaneously to get the numbers in a reasonable time. EC2 Instances are spawned from a prebuild image with user-data to assign batches to run. Instances access two S3 buckets to obtain the batch and save the results, and they should have access permissions to the buckets through IAM role.

Prerequisite

The scripts uses awscli (>=1.16.142) and boto3 (>=1.9.132).

$ pip install --user awscli boto3

AWS set up

AMI

One must first generate an AMI to run the experiments. Start with an empty Ubuntu 18.04 AMI and download this artifact.

Then, run the following commands and save the image.

$ cd <PATH_TO_ARTIFACT>
$ sudo ./aws/dependencies.sh
$ ./setup.sh
$ echo 'export PATH="<PATH_TO_ARTIFACT>/build/run/bin:$PATH"' >> ~/.bashrc
$ echo 'export PYTHONPATH="<PATH_TO_ARTIFACT>:$PYTHONPATH"' >> ~/.bashrc
$ echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib"' >> ~/.bashrc

S3 Buckets

Create two S3 buckets. BUCKET1 stores the batch configurations, and BUCKET2 stores the experiment results.

Creating S3 bucket

IAM role

Create an IAM role that gives access rights on the buckets to instances. The instances uses this role to read from BUCKET1 and write to BUCKET2.

Creating IAM role

Go to IAM, policies. Create a policy using JSON. Edit the policy boilerplate below, substituting for POLICYNAMEHERE and BUCKETNAMEHERE. This policy will allow the role to read/write files in the S3 bucket. Do this two times for BUCKET1 and BUCKET2 created above.

{
  "Version": "2012-10-17",
    "Statement": [
    {
      "Sid": "POLICYNAMEHERE",
      "Effect": "Allow",
      "Action": "s3:*",
      "Resource": "arn:aws:s3:::BUCKETNAMEHERE/*"
    },
    {
      "Sid": "VisualEditor1",
      "Effect": "Allow",
      "Action": "s3:ListAllMyBuckets",
      "Resource": "*"
    },
    {
      "Sid": "AllowRootListing",
      "Action": [
        "s3:ListBucket"
      ],
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::BUCKETNAMEHERE"
      ]
    }
  ]
}

The trust policy for your job role needs to extend to ec2 and ECS. Go to edit trust relationships and add,

{
  "Version": "2012-10-17",
  "Statement": [
  {
    "Effect": "Allow",
    "Principal": {
      "Service": "ec2.amazonaws.com"
    },
    "Action": "sts:AssumeRole"
  }
  ]
}

Security group

Create a security group.

Creating a security group

Key and Security group

Create a key pair.

Creating key pair.

Reproducing data using AWS instances

Step 1. Make batch files

$ mkdir batches
$ ./partition.py t1
$ ./partition.py t2

Step 2. Upload the batch files to S3 BUCKET1.

## Change the BUCKET1 in script `upload_to_s3.sh` to your bucket.
$ ./upload_to_s3.sh

Step 3. Run instances

This will run 200 r5.2xlarge instances in your account.

First run task T1.

## Change the REGION in script `run_instances.sh` to your AWS region.
$ ./run_instances.py t1 ami-092bba3fffa5292a7 your-security-group-id your-key-name your-iam-role-name your-BUCKET1-name your-BUCKET2-name 0 200

After above instances finish, run task T2.

## Change the REGION in script `run_instances.sh` to your AWS region.
$ ./run_instances.py t2 ami-092bba3fffa5292a7 your-security-group-id your-key-name your-iam-role-name your-BUCKET1-name your-BUCKET2-name 0 200

Step 4. Download the results from BUCKET2 to ./results-ikos.

## Change the BUCKET2 in script `download_from_s3.sh` to your bucket.
$ ./download_from_s3.sh

Step 5. Generate CSV files

## Change the BUCKET2 in script `download_from_s3.sh` to your bucket.
$ ./download_from_s3.sh
$ ./gen_table.sh t1 results-ikos
$ ./gen_table.sh t2 results-ikos

CSV files t1.csv and t2.csv will be generated in results-ikos/csv. Move them to ../data and use them to generate figures and tables.

$ mkdir ../data
$ cp results-ikos/csv/* ../data