BioGrids on AWS
BioGrids supports cloud computing on Amazon Web Services, providing scalable resources for bioinformatics workflows.
Benefits
- Scalable computing: Access unlimited computational resources
- Cost efficiency: Pay only for resources used
- Integration: Works with S3, EFS, and other AWS services
- Collaboration: Easy sharing of computational environments
Recommended Instance Types
Compute Optimized
- c5.large to c5.24xlarge: General bioinformatics workflows
- c6i instances: Latest generation compute
Memory Optimized
- r5.large to r5.24xlarge: Large genome assemblies
- x1e instances: In-memory databases, large datasets
GPU Instances
- p3 instances: Deep learning, AI/ML workloads
- g4dn instances: Graphics workstations, visualization
Quick Start
1. Launch EC2 Instance
# Launch with AWS CLI
aws ec2 run-instances \
--image-id ami-0abcdef1234567890 \
--instance-type c5.2xlarge \
--key-name your-key-pair
2. Install BioGrids
# Connect to instance
ssh -i your-key.pem ec2-user@instance-ip
# Download and install
curl -LO https://biogrids.org/downloads/latest/biogrids-cli_linux.tar.gz
tar -zxf biogrids-cli_linux.tar.gz
./biogrids-cli activate site_name username key
3. Configure Storage
# Mount EBS volume for data
sudo mkfs -t ext4 /dev/xvdf
sudo mkdir /data
sudo mount /dev/xvdf /data
# Mount EFS for shared storage
sudo mount -t efs fs-12345678.efs.region.amazonaws.com:/ /shared
S3 Integration
Data Management
# Configure AWS credentials
aws configure
# Sync data to/from S3
aws s3 sync s3://my-bucket/datasets/ /data/input/
aws s3 sync /data/results/ s3://my-bucket/output/
Direct S3 Access
# Stream data directly from S3
aws s3 cp s3://my-bucket/genome.fasta - | blast-workflow
# Parallel uploads
aws s3 cp /data/results/ s3://my-bucket/results/ --recursive
Container Integration
AWS Batch
# Register job definition
aws batch register-job-definition \
--job-definition-name biogrids-analysis \
--type container \
--container-properties '{
"image": "biogrids/analysis:latest",
"vcpus": 4,
"memory": 8192
}'
# Submit job
aws batch submit-job \
--job-name genome-analysis \
--job-queue biogrids-queue \
--job-definition biogrids-analysis
ECS/Fargate
# Task definition
family: biogrids-task
networkMode: awsvpc
requiresCompatibilities: ["FARGATE"]
cpu: "2048"
memory: "4096"
containerDefinitions:
- name: biogrids-container
image: biogrids/suite:latest
essential: true
Cost Optimization
Spot Instances
# Use spot instances for cost savings
aws ec2 request-spot-instances \
--spot-price "0.50" \
--instance-count 5 \
--launch-specification '{
"ImageId": "ami-0abcdef1234567890",
"InstanceType": "c5.2xlarge"
}'
Reserved Instances
- Purchase Reserved Instances for predictable workloads
- Use Savings Plans for flexible compute commitments
- Monitor costs with CloudWatch billing alarms
Security Best Practices
IAM Policies
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:PutObject",
"batch:SubmitJob"
],
"Resource": "*"
}
]
}
VPC Configuration
- Use private subnets for compute instances
- Configure security groups to restrict access
- Enable VPC Flow Logs for monitoring
Monitoring and Logging
CloudWatch Integration
# Install CloudWatch agent
sudo yum install -y amazon-cloudwatch-agent
# Create log group
aws logs create-log-group --log-group-name /biogrids/application
Custom Metrics
Track BioGrids-specific performance metrics through CloudWatch custom metrics and dashboards.
Getting Started
- Plan deployment: Choose instance types and storage requirements
- Set up infrastructure: Launch instances and configure networking
- Install BioGrids: Follow standard installation procedures
- Configure integrations: Set up S3 access and monitoring
- Run workflows: Deploy your bioinformatics pipelines
Support
- Documentation: Complete AWS deployment guides
- Templates: CloudFormation templates for common deployments
- Support: help@biogrids.org for AWS-specific questions
For detailed deployment assistance, contact support with your specific AWS requirements.