serverless design patterns for rethinking traditional enterprise application approaches | aws public...
TRANSCRIPT
© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Robert Groat , Smartronix @groatr
Bin Lu , Fannie Mae
June 14th, 2017
Serverless Design Patterns
“Rethinking" Traditional Enterprise Application Approaches
Agenda
- What is Serverless?
- What is AWS Lambda?
- How is it used?
- Why do I care?
- Look what Fannie Mae did!
- Total enlightenment
About Smartronix
• Premier Partner for all 5 years
• Inaugural Managed Services Partner
• Inaugural Migration Delivery Partner
• Inaugural Big Data Competency
• Inaugural DevOps Competency Partner
• 1st to bring Federal government in to AWS
• 1st to implement FISMA Moderate/ FedRAMP
solutions (NIST 800-53 Rev. 4)
• One of the largest channel resellers
• Successfully completed FedRAMP 3PAO
Assessment for Managed Services
• Named Leader in Gartner MQ for Public Cloud MSP,
Worldwide – March 2017
AWS Compute Services Overview
Service
Unit
Layer
EC2
Virtual
Machine
Hardware
ECS
App
OS
LAMBDA
Function
Runtime
Lambda Overview
Quite simply:
Run code based on events without managing servers
Serverless?
- Serverless : adjective - “1. I don’t have to manage a virtual
machine, operating system, patch management, scaling
service, load balancing, availability, fault tolerance,
provisioning, antivirus, anti-malware, vulnerability scanning,
continuous monitoring, access control, rightsizing, server
tuning, intrusion detection, hardware affinity, OS
dependencies, …ad nauseum”
AND
- I only pay for what I use!
Too good to be true…
- OK, with some limitations:
- Limited function/code size (250 MB code package)
- Asynchronous and stateless *
- 500 MB temp directory
- 300 second runtime
- 128MB to 1.5 GB Memory limitations
- 3000 concurrent function executions *
* Note: Many of these limitations are easily addressable!
“Traditional” Use Cases
- Dynamic event handling
- Scheduled event processing
- File Processing / Data Transformation
- Mobile backend services
- Streaming event sourcing / handling
- Secure REST endpoints
“Re-think”– Transformative Use Cases
- Autonomic Security Remediation
- Serverless Websites
- Dynamic log analysis
- IoT response handling
- Massively Parallel Processing???!!?
Old School Problem Solving
- Problem Statement:
- I need to run quadrillions of cash flow simulations on tens of millions of
loans every month under various economic models to determine risk.
- Old School Approach:
- Build a massive compute and shared storage infrastructure that at
capacity meets the PEAK business requirement
- License an expensive GRID control platform to orchestrate the job
scheduling and data pipelines
- Old School Result:
- Very expensive server and storage infrastructure with high management
burden and inconvenient utilization
…Slightly Less Old School Problem Solving
- Circa 2016 “Catch All” Approach
- “MOVE IT TO THE CLOUD,” says every IT talking head
- “LIFT AND SHIFT,” says every new cloud engineer
- License an expensive GRID control platform to orchestrate the job
scheduling and data pipelines
- Circa 2016 Result
- Somewhat less expensive server and storage infrastructure with high
management burden and slightly more convenient utilization patterns.
Better but not great.
Or… You can Re-Think your approach and do what Fannie Mae did!
© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Bin Lu, Fannie MaeJune 14th, 2017
High Performance Computing Using
AWS Lambda for Financial Modeling
Fannie Mae Business
Fannie Mae is a leading source of
financing for mortgage lenders:
• Providing access to affordable mortgage
financing in all market conditions.
• Effectively managing and reducing risk
to our business, taxpayers, and the
housing finance system.
In 2016, Fannie Mae provided $637B in
liquidity to the mortgage market, enabling
• 1.1M home purchase ,
• 1.4 M refinancing,
• 724K rental housing units.
Fannie Mae Financial Modeling
Financial Modeling is a Monte-Carlo simulation process to project future cash flows
which is used for managing the mortgage risk on a daily basis:
• Underwriting and valuation
• Risk management
• Financial reporting
• Loss mitigation and loan removal
~10 Quadrillion (10𝑥1015) cash flow
projections each month in hundreds
of economic scenarios.
Fannie Mae Financial Modeling Infrastructure
High Performance Computing grids are the key infrastructure components for
financial modeling at Fannie Mae.
Current Environment Issues
- No longer meets growing business needs
- 7 years old with limited non-elastic compute, storage, and IO capacity
- Costly server and storage refresh
- Complex API
- It takes more than half a year to add incremental compute capacity and
develop any new application.
Ideal New Solution Requirements
New secure capability that helps us react to the rapidly
changing market- Near infinite compute and unlimited storage with high availability
- Simple distributed computing API
- Efficient cost model
- Maximizes re-use of existing code base
- Short time to deploy solution
- Reduce operational burden – reliable and easy to manage
- Enable use of innovative services “adjacent” to our data
Fannie Mae’s Journey
In 2016, Fannie Mae began to work with AWS and Smartronix to build the first
serverless HPC computing platform in the industry using AWS Lambda. This is also
the first pilot program for Fannie Mae to develop an AWS cloud native application.
Minimal code refactoring was required and within a month we were able to run a
successful proof of concept.
By March 2017, Fannie Mae successfully deployed the first financial modeling
application to preproduction and ran on 15,000 concurrent executions
By June 2017, production migration of first workload!
Serverless HPC Reference Architecture
Map-reduce framework is used for simple parallel workload:
• Input file in S3 input bucket is split using EC2 to n triggers, which are saved in S3 event bucket.
• Lambda automatically ramps up n concurrent executions and outputs to S3 mapper bucket.
• EC2 is used to aggregate outputs and write final result to S3 reducer bucket.
Amazon S3
Input
Amazon
EC2
Splitter
…
AWS Lambda
Mappers
Amazon
EC2
Reducer
AmazonS3
Mapper
Result
Amazon
Reducer
Result…
Amazon S3
Event
Results!
Lambda service configuration:
• Initial burst rate = 3,000, incremental rate > 240
per minute, throttle limit = 15,000.
• Lambda ramps up automatically from 3,000 to
15,000 concurrent executions.
Application result:
• One simulation run of ~ 20 million mortgages takes
1.5 hours, >4 times faster than the existing process.
• Performance doesn’t degrade during ramp up period.
• Lambdas’ CPU efficiency is close to 100%. Actual elapsed time is consistent with
the estimated elapsed time based on Lambda billing time.
Number of New
Lambda Invocations
Every 5 Minutes
Maximum Concurrent
Lambdas = 15,000
HPC Grid – On Premises
Idle or constrained capacity
High CapEx costs
High maintenance burden
Performance constrained
Long time to add capacity
License fees
Long time to deliver new service
Single environment availability
Service Comparison
Serverless HPC with Lambda
Scales to meet demand
Pay per use (actual vCPU usage)
Fully managed service
Horizontal scale
Near infinite capacity on-demand
No added license fees
Rapid CI/CD – low complexity
High business resiliency
Summary
• Cloud Native thinking has potential for enormous value
• Traditional approaches can hamper your cloud adoption
• Don’t be afraid to refactor
• Establish architectural patterns with distributed systems
thinking from the start
• Serverless = Enterprise grade
• STOP DOING UNDIFFERENTIATED HEAVY LIFTING!
Focus your efforts on your code not your infrastructure.
