In this session, we will go over cloud economics and understanding the total cost of ownership (TCO) when building in the cloud and how you are trading upfront capital expenditure (CapEx) for operational expenditure (OpEx). We will also look at how the TCO changes over time as you start modernising your applications to make full use of the cloud's capabilities. Lastly, we will cover the different purchasing options to help you understand how you can reduce costs even further by identifying consistent, base workloads.

1. Cobus Bernard
Sr Developer Advocate
Amazon Web Services
Getting Started AWS:
Understanding TCO
@cobusbernard
cobusbernard
cobusbernard

2. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

3. Understanding TCO
On-
Premises
Lift & Shift Instance
Right-Sizing
Improved
Elasticity
Measure,
Monitor,
Improve
Optimized
EC2
Storage
Optimization
Serverless
Architecture
Managed
Services
True AWS
Optimized
True TCO
comparison
Typical TCO comparison

4. Invention requires two things: the
ability to try a lot of experiments,
and not having to live with
the collateral damage of
failed experiments.“
Andy Jassy
CEO, Amazon Web Services

5. You
You
Considerations when running a data centre
Spend time innovating and building new applications, not managing infrastructure
AWS
Self-managed Fully managed
Solving problems
Building applications
Focussing on product
Hardware lifecycles
Backup and recovery
Capacity planning
Industry compliance
Licensing
Networking
Cooling
Power
Physical Security

6. Challenge
They experienced service admin challenges with their original
provider and wanted to scale business to the next level.
Solution
They moved from self-managed MySQL to Amazon Aurora
MySQL. They use Aurora as the primary transactional database,
Amazon DynamoDB for personalized search, and Amazon
ElastiCache as in-memory store for sub-millisecond site rendering.
Result
Initially, the appeal of AWS was the ease of managing and
customizing the stack. It was great to be able to ramp up more
servers without having to contact anyone and without having
minimum usage commitments. AWS is the easy answer for any
Internet business that wants to scale to the next level.
—Nathan Blecharczyk, Cofounder and CTO of Airbnb
“
”
MOVE TO MANAGED →
Amazon
Aurora
Amazon
ElastiCache
Amazon
DynamoDB

7. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

8. Amazon EC2 instance characteristics
M5d.xlarge
Instance family
Instance
generation
Instance size
Instance type
CPU
Memory
Storage
Network performance
Additional
capabilities

10. Amazon EC2 general-purpose instances
M5
instances
Balance of compute, memory, and network
resources
4:1 memory-to-vCPU ratio
C5
instances
High performance at a low price per
vCPU ratio
2:1 memory-to-vCPU ratio
R5
instances
Accelerate performance for workloads
that process large datasets in memory
8:1 memory-to-vCPU ratio

11. Broadest and deepest platform choice
Workloads Capabilities Options
(AWS, Intel, AMD)
(up to 4.0 GHz)
(up to 24 TiB)
(HDD and NVMe)
(up to 100 Gbps)
(GPUs and FPGA)
(Nano to 32xlarge)
+ + =
270+instance types

12. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

13. Lost
opportunity
Infrastructure cost $
Time
Large
capital
expenditure
Opportunity
cost
Predicted demand
Traditional hardware
Actual demand
AWS
Key:
Cost savings: economics of the cloud

14. EC2 Autoscaling – Basic Example
Provisioned capacity
November

15. EC2 Autoscaling – Basic Example
Provisioned capacity
November
76%
24%

16. Autoscaling

17. To optimize Amazon EC2, combine purchase options
Use for known,
steady-state workloads
Scale using Spot for fault-tolerant,
flexible, stateless workloads
On-Demand, for new or
stateful spiky workloads

18. Amazon EC2 Cost Optimisation non-prod
100.0
71.4
35.7
29.8
0
20
40
60
80
100
24 x 7 24 x 5 12 x 5 10 x 5
% Running Time
Up to 70%
savings for non-
production
workloads

19. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

20. A serverless business strategy
Serverless-First is the decision to opt for serverless technologies
in your application as a first choice.
 No server management
 Flexible, automated scaling
 Pay for value
 Automated high availability
What do we mean when we say serverless?

21. Computing evolution – A paradigm shiftLEVELOFABSTRACTION
FOCUS ON BUSINESS LOGIC
PHYSICAL MACHINES
Requires “guess” planning
Lives for years on-premises
Heavy investments (capex)
Low innovation factor
Deploy in months

22. Computing evolution – A paradigm shiftLEVELOFABSTRACTION
FOCUS ON BUSINESS LOGIC
VIRTUAL MACHINES
Hardware independence
Faster provisioning speed (minutes/hours)
Trade capex for opex
More scale
Elastic resources
Faster speed and agility
Reduced maintenance

23. Computing evolution – A paradigm shiftLEVELOFABSTRACTION
FOCUS ON BUSINESS LOGIC
CONTAINERIZATION
Platform independence
Consistent runtime environment
Higher resource utilization
Easier and faster deployments
Isolation and sandboxing
Start speed (deploy in seconds)

24. Computing evolution – A paradigm shift
AWS Lambda
AWS Fargate
LEVELOFABSTRACTION
FOCUS ON BUSINESS LOGIC
Continuous scaling
Fault tolerance built-in
Pay for value
Zero maintenance
SERVERLESS

25. Anatomy of an AWS Lambda function
Handler() function
Function to be executed
upon invocation
Event object
Data sent during Lambda
function invocation
Context object
Methods available to
interact with runtime
information (request ID,
log group, more)
import json
def lambda_handler(event, context):
# TODO implement
return {
'statusCode': 200,
'body': json.dumps('Hello World!')
}

26. Anatomy of a serverless application
/orders
/forums
/search
/lists
/user
/...
Amazon API
Gateway
AWS Secrets
Manager /
Parameter Store
Amazon
DynamoDB
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler
Import sdk
Import http-lib
Import ham-sandwich
Pre-handler-secret-getter()
Pre-handler-db-connect()
Function myhandler(event, context) {
<Event handling logic> {
result = SubfunctionA()
}else {
result = SubfunctionB()
return result;
}
Function Pre-handler-secret-getter() {
}
Function Pre-handler-db-connect(){
}
Function subFunctionA(thing){
## logic here
}
Function subFunctionA(thing){
## logic here
}
Dependencies, configuration
information, common helper functions
Common helper functions
Business logic sub-functions
Your handler

27. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

28. AWS operational responsibility models
On premises Cloud
Less More
Compute Virtual machine
Amazon EC2 AWS Elastic Beanstalk AWS LambdaAWS Fargate
Databases MySQL
MySQL on EC2 Amazon RDS MySQL Aurora Aurora Serverless Amazon QLDB/Amazon DynamoDB
Storage Storage
Amazon S3
Messaging
Enterprise
service bus
(ESB) Amazon MQ Amazon Kinesis Amazon Event Bridge / SNS / SQS
Analytics
Hadoop Hadoop on EC2 Amazon EMR Amazon ES Amazon Athena

29. You
You
Fully managed services on AWS
Spend time innovating and building new applications, not managing infrastructure
AWS
Self-managed Fully managed
Schema design
Query construction
Query optimization
Automatic failover
Backup and recovery
Isolation and security
Industry compliance
Push-button scaling
Automated patching
Advanced monitoring
Routine maintenance
Built-in best practices

30. Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Aurora, RDS
Key-value
High
throughput,
low-latency
reads
and writes,
endless scale
Real-time
bidding, shopping
cart, social,
product catalog,
customer
preferences
DynamoDB
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
DocumentDB
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time
analytics, caching
ElastiCache
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social
networking,
recommendation
engine
Neptune
Time-series
Collect, store,
and process
data
sequenced
by time
IoT applications,
event tracking
Timestream
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Systems
of record, supply
chain, health care,
registrations,
financial
QLDB
AWS
Service(s)
Common Use
Cases
Data Model and Store

31. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

32. Architecture evolution
Monolithic application
Does everything
Shared release pipeline
Rigid scaling
High impact of change
Hard to adopt new technologies
Microservices
Does one thing
Independent deployments
Independent scaling
Small impact of change
Choice of technology
When the impact of change is small, release velocity can increase

33. Staff productivity: AWS benchmarking insights
Source: n = 1036 AWS customers. AWS Cloud Economics Benchmarking, 2019.
Cloud improves efficiency… With larger gains for re-architected applications
57.9%
Increase in # of VMs
managed per admin
153.5%
Increase in # of TB
managed per admin
over time
67.7%
Increase in # of TBs
managed per admin
147.7%
Increase in # of VMs
managed per admin
over time
All Customers All Customers Apps that were re-architected,
re-factored, or re-platformed
Apps that were re-architected,
re-factored, or re-platformed
On-premises In cloud On-premises In cloud On-premises In cloud On-premises In cloud

34. Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cobus Bernard
Sr Developer Advocate
Amazon Web Services
@cobusbernard
cobusbernard
cobusbernard