[Games on AWS 2019] AWS 입문자를 위한 초단기 레벨업 트랙 | AWS 레벨업 하기! : 컴퓨팅 - 조용진 AWS 솔루션즈 아키텍트

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS 레벨업 하기! : 컴퓨팅
조용진 솔루션즈 아키텍트, AWS

Broadest customer base for compute
Enterprise Public SectorStartup Game
“Epic Games uses a range of AWS services to provide
the availability it needs to support peak usage more
than 10 times that of non-peak, as well as the
scalability to host game events with all of its 200
million users invited.”
—Chris Dyl, Director of Platform, Epic Games

Customer obsessed
of roadmap originates with customer requests
90%
“Performance, reliability, and responsiveness are fundamental
to our customer experience, and T3 instances help us to deliver on
that customer promise while also controlling our costs.”
—Heroku

Built to support
virtually every
workload
EC2 Fundamentals
Lambda Fundamentals

Amazon EC2
Broadest and deepest platform choice
Virtual server instances
in the cloud
Amazon ECS,
EKS, and Fargate
Container management
service for running
Docker on a managed
cluster of EC2
AWS Lambda
Serverless compute
for stateless code execution
in response to triggers

Linux | Windows
Arm and x86 architectures
Bare metal, disk, networking capabilities
Packaged | Custom | Community AMIs
General purpose and workload optimized
Multiple purchase options: On-demand, RI, Spot
Amazon EC2

Innovate
Upgrade and
modernize
Familiar
experiences10 years experience of Windows in AWS
Full Windows stack support
Active Directory, .NET, System Center, Microsoft SQL Server
Latest versions of Windows and SQL Server
Managed offering with Relational Database Service
License flexibility including Bring Your Own License
Most proven platform for Windows workloads

Amazon Elastic Compute Cloud (EC2)
Virtual servers in the cloud
Physical servers in
AWS global regions
Guest 1 Guest 2 Guest n
Hypervisor
Host server
EC2 instances

Amazon EC2 12+ years ago…
Scale up or down
quickly, as needed
Pay for what
you use “One size fits all”
M1

Continued rapid pace of innovation
Instance growth
instances
2007 2018

Nitro Card Nitro Security Chip Nitro Hypervisor
Local NVMe storage
Elastic Block Storage
Networking, monitoring,
and security
Integrated into motherboard
Protects hardware resources
Lightweight hypervisor
Memory and CPU allocation
Bare metal-like performance
Innovation enabled by AWS Nitro System
Modular building blocks for rapid design and delivery of EC2 instances

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

Amazon EC2 인스턴스의 크기
c5.8xlarge c5.4xlarge x 2
≈
c5.2xlarge x 4
≈
c5.xlarge x 8
≈

Amazon maintained
Broad set of Linux and
Windows images
Kept up-to-date by Amazon
in each region
Marketplace
maintained
Managed and maintained
by AWS Marketplace
partners
Your machine images
AMIs you have created from
Amazon EC2 instances
Can keep private, share with
other accounts, or publish to
the community
Amazon Machine Images (AMIs)
Amazon Linux 2 with five
years of long term support

Broadest choice of processors and architectures
Intel® Xeon® Scalable
(Skylake) processor
AMD EPYC processor
Right compute for the right application and workload
AWS Graviton
Processor

AMD instances for cost-optimized compute
• R5, M5, and T3 instances with AMD EPYC processors
10% cost savings
compute and memory
Seamless application
migration
Ideal for general purpose and memory optimized workloads

AWS support for Arm-based applications
Optimized cost and performance for Arm-based applications
Up to 45% cost savings,
higher price/performance
AWS Graviton Processor
with Arm-based cores and
customized silicon
A1
Ideal for scale-out workloads including web and e-commerce sites

General purpose instance workloads
Web/app servers Enterprise apps Gaming servers
Caching fleets Analytics applications Dev/test environments

M5: General purpose instances
• Balance of compute, memory, and networking resources
• Powered by 2.5 GHz Intel Xeon Scalable Processors (Skylake)
• Largest instance size, m5.24xlarge has 96 vCPUs and 384 GiB of
memory
• Improved network and EBS performance on smaller sizes
• Support for Intel AVX-512 offering up to twice the performance
for vector and floating point workloads
14% price/performance
improvement with M5
계열1 계열2
M5d
SSD
M5d: Now available with high
performance local NVMe SSD
storage
M5a
M5a: Now available with AMD
EPYC 7000 processor for 10%
lower costM4 M5

Low utilization
High utilization
Opportunity: Most instances aren’t very busy

T3: Burstable general-purpose instances
• Balance of compute, memory, and network
• Baseline level of CPU performance with the
ability to burst CPU usage when needed at
any time for as long as required
• Lowest cost instance at $0.0052 per hour
and up to 30% better price performance
over T2
With T3 Unlimited
bursting over
baseline is only
$0.05 per vCPU-
hour, averaged over
24 hours
…7 sizes
t3.nano
0.5 GiB
2 vCPU
Base perf 5%
t3.2xlarge
32 GiB
8 vCPU
Base perf 40%
T3a: Coming soon with
AMD EPYC 7000
processor for 10% lower
cost
T3a

R5: Memory optimized instances
In-memory caches High performance databases Big data analytics
Memory-optimized instances with 8:1 GiB to vCPU
2.5 GHz Intel Xeon Scalable Processors (Skylake)
Up to 25 Gbps NW bandwidth
R5d instances include up to 3.6 TB of local NVMe SSD
r5.large
16 GiB
2 vCPU
r5.24xlarge
768 GiB
96 vCPU
…
6 sizes
R5a: Now available with AMD
EPYC 7000 processor for 10%
lower cost
R5a
R5.metal Bare Metal instances
coming soon

X1 and X1e: Large-scale memory-optimized
For memory-intensive workloads and very large
in-memory workloads
32:1 GiB to vCPU ratio
High-performance databases, Large in-memory databases (e.g. SAP
HANA), and DB workloads with vCPU based licensing (Oracle, SAP)
For large in-memory workloads
In-memory databases (e.g., SAP HANA), big data processing engines
(Apache Spark, Presto), in-memory analytics
x1.16xlarge
1 TB
64 vCPU
x1.32xlarge
2 TB
128 vCPU
x1e.xlarge
122 GiB
4 vCPU
x1e.32xlarge
4 TB
128 vCPU
…6 sizes
X1 X1e

SQL
I3: I/O optimized instances
9X as many IOPS
as I2
계열1 계열2
Intel Xeon E5 v4 (Broadwell) processors, with up to 15.2 TB of locally
attached NVMe SSD storage, 64 vCPUs, and 488 GiB memory
Lowest cost per IOPS ($/IOPS)
Offers very high Random I/O (up to 3.3 million IOPS) and disk throughput
(up to 16 GB/s)
Up to 25 Gbps NW bandwidth
Available in Bare Metal, with i3.metal
Transactional workloads
High-perf databases
No SQL databases
Real-time analytics
I2 I3

D2 and H1: Dense storage workloads
Lowest cost per storage
($/GB)
Supports high sequential
disk throughput
More vCPUs and memory per
terabyte of disk
Lower costs for big data
uses cases
Data warehousing
HDFS
Log processing
Big data
Kafka
MapReduce
d2.8xlarge
244 GiB
36 vCPU
48 TB HDD
h1.16xlarge
256 GiB
64 vCPU
16 TB HDD

Compute-intensive workloads
Batch processing Distributed analytics High-perf computing (HPC)
Ad serving Multiplayer gaming Video encoding

C5: Compute-optimized instances based on Intel Skylake
• Custom 3.0 GHz Intel Xeon Scalable Processors (Skylake)
• Up to 72 vCPUs and 144 GiB of memory (2:1 Memory:vCPU
ratio)
• 25 Gbps network bandwidth
• Support for Intel AVX-512
• C5d with local NVMe-based SSD storage
“We saw significant performance improvement on
Amazon EC2 C5, with up to a 140% performance
improvement in industry standard CPU benchmarks over
C4.”
“We are eager to migrate onto the AVX-512 enabled
c5.18xlarge instance size…
We expect to decrease the processing time of some of
our key workloads by more than 30%.”
50
60
70
80
90
100
110
120
130
25% price/performance
improvement over C4
계열1 계열2C4 C5

C5n: Fastest networking in the cloud
33% Increased memory footprint over C5 instances
25 Gbps peak bandwidth on smaller instance sizes
100 Gbps network bandwidth on largest instance sizes
Faster analytics and big
data workloads
Lower costs for
network-bound
workloads
All of the elasticity,
security, and scalability
of AWS
C5n

z1d: High frequency for specialized workloads
High Frequency instances with custom
Intel® Xeon® Scalable Processors running
at sustained 4 GHz all core turbo
Up to 25 Gbps network bandwidth and
up to 1.8 TB of local NVMe storage
Electronic Design Automation Relational databases Gaming
z1d.large
16 GiB
2 vCPU
…
z1d.12xlarge
384 GiB
48 vCPU
…6 sizes

Accelerated Computing Workloads
Applications that benefit from GPU and FPGA Acceleration
Natural Language
Processing
Image and Video
recognition
Autonomous
vehicle systems
Recommendation
Systems
Computational Fluid
Dynamics
Financial and Data
Analytics
Genomics Computational
Chemistry
Virtual Graphic
Workstation
3D Modeling &
Rendering
Video Encoding AR/VR
Machine Learning/AI High Performance Computing Graphics

P3 instances: GPU Compute
Ideal for workloads needing
massive parallel processing power
Training Machine Learning Model
Running HPC Simulations
Rendering 3D models
Video encoding
P3.2xlarge
1 V100
GPU
8 vCPU
61 GB
Mem
P3.8xlarge
4 V100
GPU
32 vCPU
244 GB
Mem
P3.16xlarge
8 V100
GPU
64 vCPU
488 GB
Mem
Up to eight NVIDIA Tesla V100 GPUs
1 PetaFLOPs of computational performance—Up to
14x better than P2
300 GB/s GPU-to-GPU communication (NVLink)—9X
better than P2
Support all ML frameworks and model types
p3dn.24xlarge
8 V100
GPU
96 vCPU
2 TB NVME SSD
768 GB
Mem
100 Gbps Throughput
P3dn - Most powerful GPU instance in the cloud
Efficiently scale ML model training and HPC simulations across multiple
instances with 100Gbps of networking throughput
Fast access to training or simulation data via Amazon S3, network attached file
systems or local instance storage
Train larger ML models or process more data via latest NVIDIA V100 GPU with
32GB of GPU memory
Optimize pre-processing of data with 96 vCPU using AWS Custom Skylake CPUs
and 768GB of System Memory

New smaller size - Cost and performance
optimized for remote workstations
Seismic exploration and
analytics for oil and gas
“The exploration and production
models are increasingly complex with
very large datasets, 3D and dynamic
algorithms, security, and global
reach... . Amazon EC2 G3 instances
enable Landmark to deliver value to
our clients in ways that were not
possible before.”
- Chandra Yeleshwarapu,
Global Head of Services and Cloud
Landmark, Halliburton
G3 instances: High Performance Graphics
Ideal for workloads needing
massive parallel processing power
• Graphic Visualizations
• Cloud workstation
• Video encoding
• Virtual reality
4 GPUs, 64 vCPUs, 488 GiB of host
memory, and 20 Gbps of network
bandwidth
Tesla M60 GPU with included
support for GRID Virtual
Workstation features and licenses,
and supports up to four monitors
with 4096x2160 (4K) resolution
Available in 4 Sizes
G3.4xlarge
1 M60
GPU
8 vCPU
61 GB
Mem
G3.8xlarge
2 M60
GPU
32 vCPU
244 GB
Mem
G3.16xlarge
4 M60
GPU
64 vCPU
488 GB
Mem
G3s.xlarge
1 M60
GPU
4 vCPU
30.5 GB
Mem
Seismic Imaging

Elastic Inference
Easily add graphics acceleration to
your EC2 instance
Configure right amount of graphics
acceleration for your workload
Accelerate application for fraction of
cost of standalone graphics instances
Elastic Graphics
Reduce deep learning inference costs
by up to 75%
Easily attach fractional sizes of a full GPU
instance to EC2 or SageMaker instances
Scale inference acceleration up or down
as needed with EC2 Auto Scaling
Choice of accelerators for specialized workloads

F1 Instances: First Cloud Instance with FPGA
Accelerators
Speed up applications
over 30x using hardware
acceleration
• Genomics sequencing
• Financial computing
• Engineering simulations
• Image and video processing
• Big data and ML
• Security, compression
New – f1.4xlarge size to optimize
price/performance
2. Use Pre-built Accelerators from F1 Partners
F 1 i n s t a n ce
W i t h y o u r c u s t o m
l o g i c
D e v e l o p , s i m u l a t e , d e b u g
& c o m p i l e y o u r c o d e
Package as FPGA
Images
1. Develop Custom Accelerators or

Categories Capabilities Options
+ + =
200+instance types

Amazon EC2 Resources
EBS EBS EBS
VPCVPC
EBS EBS EBS
EBS
Snapshots
Amazon S3
Instance
Virtual Machine
Configuration
Running or Stopped
VM
AMI

Simplifying capacity and cost optimization
Use Reserved Instances
for known/steady-state workloads
Scale using
Spot,
On-Demand,
or both
AWS services make this easy and efficient
Now with hibernate on-demand
Amazon Elastic
Container Service
Amazon
EMR
AWS
CloudFormation
AWS BatchAmazon EC2
Auto Scaling
Amazon Elastic
Container Service
for Kubernetes
EC2 Fleet AWS
Thinkbox

Ramp capacity before you need it with
Predictive Scaling
On-premises capacity
provisioning
Time
Load/capacity
Capacity provisioning with target tracking
Time
Load/capacity
Capacity provisioning with predictive
scaling and target tracking
Time
Load/capacity
Accommodates long warm-ups by bringing up instances ahead of spikes
Automatically tracks changing traffic patterns with changing usage patterns
Prevents downscaling during sudden untimely traffic dips

Simplify rendering
at scale for media
and entertainment
Customer story:
Milk
Creating massive ocean and
storm simulations for Adrift

AWS Lambda
Bring your own code
Simple Resource model
Flexible authorization
Stateless
Flexible use
Fine-grained pricing
AWS Lambda

No servers to provision
or manage
Automatic Scaling
Never pay for idle Highly available and secure
What is serverless?
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

AWS
Lambda
AWS
Fargate
Amazon
API Gateway
Amazon
SNS
Amazon
SQS
AWS
Step Functions
COMPUTE
DATA STORES
INTEGRATION
Amazon Aurora
Serverless
Amazon
S3
Amazon
DynamoDB
AWS
AppSync

Serverless compute engine
for containers
Long-running
Bring existing code
Fully-managed orchestration
AWS Fargate
Let’s focus on compute for now
Serverless event-driven
code execution
Short-lived
All language runtimes
Data source integrations
AWS Lambda

Comparison of operational responsibility
AWS Lambda
Serverless functions
AWS Fargate
Serverless containers
ECS/EKS
Container-management as a service
EC2
Infrastructure-as-a-Service
More opinionated
Less opinionated
AWS manages Customer manages
• Data source integrations
• Physical hardware, software, networking,
and facilities
• Provisioning
• Application code
• Container orchestration, provisioning
• Cluster scaling
• Physical hardware, host OS/kernel,
networking, and facilities
• Security config and updates, network config,
management tasks
• Container orchestration control plane
• Physical hardware software,
• Work clusters
firewall, management tasks
• Physical hardware software,
• Scaling
management tasks
• Provisioning, managing scaling and
patching of servers

Making development easier with AWS Lambda
Trillions of executions every month for hundreds of thousands of active customers
Accessible for
all developers
Enable new
application patterns
Greater
productivity
Support for all runtimes
with Lambda Layers and Runtime API
ISO, PCI, HIPAA, SOC, GDPR,
and FedRamp compliances
15 minute functions
SQS for Lambda
Automatic Load Balancing for Lambda
Support for Kinesis Data Streams Enhanced
Fan-Out and HTTP/2
Toolkits for popular IDEs:
VSCode, IntelliJ, and PyCharm
Simplified deployment
with nested apps

Serverless applications
Services (anything)
Changes in
data state
Requests to
endpoints
Changes in
resource state
Event source Function
Node.js
Python
Java
C#
Go
Ruby
Runtime API!

Anatomy of a 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!')
}

Lambda Layers
Lets functions easily share code: Upload layer once,
reference within any function
Promote separation of responsibilities, lets developers
iterate faster on writing business logic
Built in support for secure sharing by ecosystem

Using Lambda Layers
• Put common components in a ZIP file and
upload it as a Lambda Layer
• Layers are immutable and can be versioned
to manage updates
• When a version is deleted or permissions to
use it are revoked, functions that used it
previously will continue to work, but you
won’t be able to create new ones
• You can reference up to five layers, one of
which can optionally be a custom runtime
Lambda
Layers
arn:aws:lambda:region:accountId:layer:shared-lib
Lambda
Layers
arn:aws:lambda:region:accountId:layer:shared-lib:2
Lambda
Layers
arn:aws:lambda:region:accountId:layer:shared-lib:3

Lambda Runtime API
Bring any Linux compatible language runtime
Powered by new Runtime API - Codifies the
runtime calling conventions and integration
points
At launch, custom runtimes powering Ruby
support in AWS Lambda, more runtimes from
partners (like Erlang)
Custom runtimes distributed as “layers”
Rule

Common Lambda use cases
Web
Applications
• Static
websites
• Complex web
apps
• Packages for
Flask and
Express
Data
Processing
• Real time
• MapReduce
• Batch
Chatbots
• Powering
chatbot logic
Backends
• Apps &
services
• Mobile
• IoT
</></>
Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa Skills Kit
IT
Automation
• Policy engines
• Extending
AWS services
• Infrastructure
management

Lambda execution model
Synchronous Asynchronous Poll-based
/order
Events
Changes
AWS Lambda
service
Lambda function
Amazon
API Gateway
Amazon SNS Amazon S3
Lambda function
Amazon
DynamoDB
Amazon
Kinesis
Lambda
function

Event sources that trigger Lambda
Synchronous Asynchronous Poll-based
Elastic Load
Balancing
Amazon
Cognito
Amazon Lex Amazon Alexa
Amazon
API Gateway
Amazon
CloudFront
Amazon Kinesis
Data Firehose
Amazon S3 Amazon SNS
Amazon SES AWS
CloudFormation
Amazon
CloudWatch Logs
Amazon
CloudWatch Events
AWS
CodeCommit
AWS Config
Amazon Kinesis Amazon SQS
Amazon
DynamoDB
Streams

Fine-grained pricing
Buy compute time in 100ms increments
Low request charge
No hourly, daily, or monthly minimums
No per-device fees
Never pay for idle
Free Tier
1M requests and 400,000 GB of compute.
Every month, every customer.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Keep orchestration out of code
Track status of data
and execution
Remove
redundant code

AWS Step Functions
• “Serverless” workflow management with
zero administration
• Makes it easy to coordinate the components
of distributed applications and microservices
using visual workflows
• Automatically triggers and tracks each step
and retries when there are errors, so your
application executes in order and as
expected
• Logs the state of each step, so when things
do go wrong, you can diagnose and debug
problems quickly
Task
Choice
Failure capture
Parallel tasks

Step Functions: Integrations
Simplify building workloads such as order processing,
report generation, and data analysis
Write and maintain less code; add services in minutes
More service integrations:
AWS Step
Functions
Amazon
Simple
Notification
Service
Amazon
Simple Queue
Service
Amazon
SageMaker
AWS Glue AWS Batch Amazon Elastic
Container
Service
AWS Fargate

Simpler integration, less code
• With serverless polling • With direct service integrationStart
End
AWS
Lambda
functions
Start
End
No
Lambda
functions

AWS Korea 공식 소셜 채널에서
발표자료 및 녹화 동영상을 확인하세요.
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참여해 주신 모든 분들께
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입문자 트랙 강연을 듣고, AWS 공식 교육 바우처를 받으세요!
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2
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*강연 시작 후 10분 이내에 ‘입장’ & 강연 종료 후 ‘퇴장’
*선착순 100명 한정
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[Games on AWS 2019] AWS 입문자를 위한 초단기 레벨업 트랙 | AWS 레벨업 하기! : 컴퓨팅 - 조용진 AWS 솔루션즈 아키텍트

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