Learning Objectives: - Learn about the options for running batch workloads on AWS - Learn how to architect a containerized batch processing service on Amazon ECS - Learn best practices for optimizing and scaling complex batch workload requirements Batch processing is useful when you need to periodically analyze large amounts of data, but configuring and scaling a cluster of virtual machines to process complex batch jobs can be difficult. Containers provide a great solution for running batch jobs by providing easily managed, scalable, and portable code environments. In this tech talk, we’ll show you how to use containers on AWS for batch processing jobs that can scale quickly and cost-effectively. We’ll discuss AWS Batch, our fully managed batch-processing service, and show you how to architect your own batch processing service using the Amazon EC2 Container Service. We’ll also discuss best practices for ensuring efficient and opportunistic scheduling, fine-grained monitoring, compute resource auto-scaling, and security for your batch jobs.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Asif Khan, Technical Business Development
Manager
6/14/2017
Running Batch Processes on ECS

2. • Batch processing – overview and challenges
• Why run batch workloads in the cloud
• Overview of AWS batch solutions
• Deep dive look at AWS Batch and Amazon ECS
• Best practices review
Agenda

3. Challenges of Running Batch Workloads
• Typically resource intensive
• Time constraint for completion
• Potential impact to concurrent batch jobs
• Scaling infrastructure resources
• Ensuring effective resource utilization and cost savings
• Fragile and unreliable

4. What Batch Workloads Need
Reliability Easy Development Easy Deployment
High Efficiency Low Ops Load Cost Effective

5. Why the cloud makes sense for batch workloads
Reliable Scalable Pay as you goInfrastructure as
code

6. Why containers make sense for batch workloads
• Simple to model
• Polyglot
• Image is the version
• Do one thing well
• You build it, you run it
• Black box

7. Options for batch workloads on AWS
AWS Batch Amazon ECS

8. Introducing AWS Batch
• Fully managed batch primitives
• Focus on your applications (shell scripts,
Linux executables, Docker images) and
their resource requirements
• We take care of the rest!

9. IAM role for the
AWS Batch job
Input files
Queue of
runnable jobs
S3 events trigger a
Lambda function that submits
an AWS Batch job
AWS Batch
compute environments
AWS Batch
job output
Typical AWS Batch Job Architecture
Job definition
Job resource requirements
and other parameters
AWS Batch execution
Application
image
AWS Batch
Scheduler

10. Amazon EC2 Container Service (ECS) is a highly
scalable, high performance container management
service that supports Docker containers and allows you
to easily run applications on a managed cluster of
Amazon EC2 instances.
Introducing Amazon ECS

11. Cluster Management Made Easy
Nothing to run
Complete state
Control and monitoring
Scale

12. Performance at Scale

13. Flexible Container Placement
Applications
Batch jobs
Multiple schedulers

14. Designed for Use with Other AWS Services
Elastic Load Balancing
Amazon Elastic Block Store
Amazon Virtual Private Cloud
AWS Identity and Access Management
AWS CloudTrail
Spot Fleet

15. Security
Your own EC2 instances in a VPC
with all its security features to
provide a high level of isolation.

16. How ECS works
EC2 INSTANCES
LOAD
BALANCER
Internet
ECS
AGENT
TASK
Container
TASK
Container
ECS
AGENT
TASK
Container
TASK
Container
AGENT COMMUNICATION
SERVICE
Amazon
ECS
API
CLUSTER MANAGEMENT
ENGINE
KEY/VALUE STORE
ECS
AGENT
TASK
Container
TASK
Container
LOAD
BALANCER

17. Running batch workloads on
ECS

18. File put into
S3 bucket
Amazon
Simple Queue
Service
Output to S3
bucket
Amazon ECS provisions compute
clusters and schedules tasks based
on demand
Batch worker
task polls
SQS for new
jobs
Queue load is
communicated to
ECS
Containerized
batch worker
processes file
Basic batch workflow with ECS

19. Trigger Batch Processing with Lambda
Amazon ECS
Availability Zone Availability Zone
Container Instance
AutoScaling Group
Task A
AWS Lambda
Amazon
S3 Bucket
(Source)
ecs:RunTask
Amazon
S3 Bucket
(Target)
Amazon
S3 Bucket
Object
Amazon
CloudWatch
AWS CloudTrail
Container Instance

20. Fleet of workers with ECS with SQS
Amazon ECS
Availability Zone Availability Zone
SQS queue
Container Instance Container Instance
AutoScaling Group
Task A
AWS Lambda
Amazon
S3
DynamoDB
Amazon
Kinesis
ecs:RunTask
Amazon
CloudWatch
AWS CloudTrail

21. Long-running Batch Jobs
• Utilize Spot
Instances
• EC2 Spot Blocks for
Defined-Duration
Workloads
• ECS event stream
for CloudWatch
Events
• Service Scaling and
Monitoring
Amazon ECS
Availability Zone Availability Zone
Container Instance Container Instance
AutoScaling Group
Task A Task B
Task C
Amazon
CloudWatch
AWS CloudTrail

22. Get the Best Value for EC2 Capacity – Spot
Instances
• Since Spot instances typically cost 50-90% less than
On-Demand, you can increase your compute capacity by
2-10x within the same budget
• Or you could save 50-90% on your existing workload
• Either way, you should try it!

23. Best Practices
• Store state and inputs, outputs in S3 or another
datastore
• Minimize dependencies between task definitions (should
be independent of each other)
• Use Spot Instances and Spot fleets for long-running
batch jobs
• Monitor cluster state with ECS APIs
• Share pools of resources
• Auto Scaling, VPC, IAM, scheduled Reserved Instances

25. Serving
Maps at
Scale on
AWS

26. C4 R3 M4R3 R3
R3 R3 R3
M4 M4
M4 M4 M4
C4 C4
C4 C4 C4
Map Service Search Service Directions Service

27. C4
ECS Cluster
R3 M4R3 R3
R3 R3 R3
M4 M4
M4 M4 M4
C4 C4
C4 C4 C4
Map Service Search ServiceDirections Service

28. Deploying
with the
Old Way
R3 R3 R3
R3 R3 R3
Git SHA 56fb514
R3 R3 R3
R3 R3 R3
Git SHA 168f73e

29. Deploying
on ECS

30. Reduce
Waste with
Better
Instance
Packing

31. Stability On
Spot with
Instance
Diversity
C4
ECS Cluster
R3 R3 R3
R3 R3 R3
M4
M4
M4 M4
M4 M4
C4 C4
C4 C4 C4
Map Service Search ServiceDirections Service
Spot Fleet
C4
C4
R3
R3

32. 25%
Fewer instances
80-90%
Savings per month on EC2

33. 21
Services
2000
Tasks
1.3 billion
Requests per day

34. Time and Event-Based Task Scheduling
• schedule on fixed time intervals (e.g.: number of minutes, hours,
or days)
• Or use cron expressions
• Set Amazon ECS as a CloudWatch Events target

35. Time and Event-Based Task Scheduling
• schedule on fixed time intervals (e.g.: number of minutes, hours,
or days)
• Or use cron expressions
• Set Amazon ECS as a CloudWatch Events target

36. Summary
• Cloud and containers are a great way to run batch
workloads
• Two options on AWS: Batch and ECS
• Why AWS Batch:
• Managed Batch Processing environment
• Why ECS:
• DIY Batch Processing
• Very flexible Time and Event based Task Scheduling

37. Thank you!