This session covers the latest Amazon EC2 features and capabilities, including new instance families available in Amazon EC2, the Amazon EC2 Nitro system architecture, the differences among their hardware types and capabilities, and their optimal use cases. We also cover best practices on how to optimize your expenditure on Amazon EC2 to make the most of your EC2 instances, saving you time and money.

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EC2 Foundations; Autoscaling –
The tip of the cost optimisation
Iceberg
Simon Treacy
Solution Architect
AWS
E C C 1

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Amazon EC2 foundations
Agenda
• What is EC2?
• How to identify different EC2 options
• Examples of EC2 uses
• A working example of cost reduction
• Changes requiring little technical skill – but make a big cost saving

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AWS Global Infrastructure
• 21 Regions with 64 Availability Zones
• 4 Regions coming soon: Bahrain,
Cape Town, Jakarta and Milan
• 100Gbps redundant network
• 99.99% SLA

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Amazon EC2 foundations
Resources
Instances
Storage
Networking
Availability
Regions and AZs
Placement Groups
Load Balancing
Auto Scaling
Management
Deployment
Monitoring
Administration
Purchase Options
On Demand
Reserved
Spot

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Choice of processors and architectures

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Amazon EC2 instance characteristics
M5a.xlarge
Instance family
Instance
generation
Instance size
Instance type
CPU
Memory
Storage
Network performance
*Additional
capabilities

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Continued rapid pace of innovation
Instance growth Instances
2007 2018

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Broadest and deepest platform choice
Categories Capabilities Options
Choice of processor
(AWS, Intel, AMD)
Fast processors
(up to 4.0 GHz)
High memory footprint
(up to 12 TiB)
Instance storage
(HDD and NVMe)
Networking
(up to 100 Gbps)
Accelerated computing
(GPUs and FPGA)
Bare Metal
Size
(Nano to 32xlarge)
Amazon Elastic
Block Store
Elastic Inference
Elastic Graphics+ + =
Compute intensive
Memory intensive
Burstable
Storage (High I/O)
Dense storage
GPU compute
Graphics intensive
General purpose
185+instance types
for virtually
every workload
and business need

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Amazon EC2 use cases
Scalability: Eliminate stranded costs
from sold assets through dynamic
resource allocation.
Reduce over-provisioning: Increase in
utilization from 7-8% to 45-50%.
Co-innovation: Working with AWS to
automate major processes.
Why did BP migrate SAP to AWS?
Steve Fortune
Group CIO
BP needs the agility to be competitive when
prices, policy, technology and customer
preferences are changing - and that's what we
get with AWS. Recently, we fully provisioned
220 virtual machines on AWS - from initial
planning to app design layout, provisioning and
production - in one week.

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Compute is at the heart of immense scale
Natural Language Processing on 1.1 million concurrent vCPUs
in less than 24 hourshttps://amzn.to/2hNXMXi

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EC2 Autoscaling – Basic Example
Provisioned capacity
November

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

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Amazon EC2 Cost optimization –
Tip of the iceberg

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EC2 Autoscaling – Rightsizing
More smaller instances vs. fewer larger instances
29 m5.large @ $0.111 /hr
$2,349.87 / mo*
59 t3.medium @ $0.0472/hr
$2,032.90 / mo*
*Assumes Linux instances in the EU (London) Region at 730 hours per month
13.49% Saving

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Amazon EC2 Reserved Instances
Commitment level
1 year
3 year
* Dependent on specific AWS service, size/type, and region
AWS services offering RIs
Amazon EC2
Amazon RDS
Amazon DynamoDB
Amazon Redshift
Amazon ElastiCache

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EC2 – Reserved Instances

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EC2 – Reserved Instances

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EC2 – Reserved Instances

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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

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AWS Instance Scheduler
• AWS-provided solution
• Custom start & stop schedules
• Works with EC2 & RDS instances
• Deploy using CloudFormation
• Selectively tag instances to schedule
• Multiple schedules per instance
• 5-minute granularity
https://aws.amazon.com/answers/infrastructure-
management/instance-scheduler/

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EC2 Hibernation

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Amazon EC2 Spot

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Amazon EC2 Fleet
Benefits
Synchronously provision capacity across different instance types, AZs, and purchase
options with a single API
Reduce costs
Increase operational efficiency
Flexible capacity allocation
Massive scale
Simplified provisioning
Spot
Instances
On-Demand
Instances
On-Demand
Instances
Spot
Instances
Reserved
Instances
Reserved
Instances
AZ1 AZ2
Use all three purchase options to optimize costs
Automatic optimization behind the scenes with machine
learning
Instant Fleets: Drop-In
replacement for RunInstances
Reduce development effort

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Include Spot, On-Demand and RIs in a Single ASG
m4.large Spot Instances
m5.large Reserved Instances
c4.xlarge On-Demand instances
Availability
Zone 1
Availability
Zone 2
Availability
Zone3
Purchase options,
instance types and
AZs in a single ASG
Single ASG

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Compute at scale
“Storage technology is amazingly complex and we’re constantly pushing
the limits of physics and engineering to deliver next-generation capacities
and technical innovation. This successful collaboration with AWS shows
the extreme scale, power and agility of cloud-based HPC to help us run
complex simulations for future storage architecture analysis and materials
science explorations.
Using AWS to easily shrink simulation time from 20 days to 8 hours allows
Western Digital R&D teams to explore new designs and innovations at a pace
un-imaginable just a short time ago.”
—Steve Philpott, CIO, Western Digital

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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

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Advertise.
Attribute.
Act.
Cost Optimising AWS

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The worldwide leader in TV
attribution
TVSquared is trusted by thousands of brands,
agencies and networks in more than
70 countries

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About TVSquared
About TVSquared
Make TV a
performance-
marketing channel
Use real-time analytics to uncover TV’s direct impact on
KPIs, including sales, site traffic, search, app activity, SMS
and more
Understand spot-level and campaign-wide performance
by channel, program, daypart, day of week, creative,
genre, etc.
Improve and optimize TV buying with automated buy
recommendations
Prove the performance of advanced TV and leverage
deterministic data to see TV’s impact down to the
household level

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Measure TV Like Digital
About ADvantage
Measure the immediate and
longer-term impact of TV
Get spot- and campaign-level performance
analytics by day, daypart, network, genre,
program, creative, audience and household
Calculate TV-driven conversions and campaign
ROI
Track how TV drives response via phone, app,
mobile, web and SMS
Get a global view of TV with multi-country and
multi-brand analytics

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Scalability/Data Capacity
Why TVSquared?

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TVSquared Processing
About TVSquared
Peak volume ~ 10,000 hits / second
Custom job-queue processing ~ 2M jobs / month
Linear increase in complexity
Infrastructure cost keeping pace with revenue
Processing Volume
Processing Complexity

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Equating Cost to Business Activity
Why TVSquared?

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Cost Allocation Algorithm
Raw AWS
billing
data
Usable cost
groupings
… sliced by
client….
… then clients grouped to
give actionable insight
Tagging and
environment
labelling in
AWS
Usage
Stats
Client
Features

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Estimates are better than nothing
Cost Area Actual Driver Best, Actually Available, Proxy Proxy Accuracy
Collector Max Hits at Peak time Average Growth rate of PG Database - Total Size/DB Age in Days Terrible
FrontEnd Mongo Actual Mongo DB Size Actual Mongo DB Size Excellent
FrontEnd Other Usage of Portal Whatever - this is small - use Mongo DB Size Whatever
Backend PG Data Transfer Processing Data Throughput PG DB Size (across all Servers) OK
Backend Mongo Actual Mongo DB Size Actual Mongo DB Size Excellent
Backend Workers Usage of Worker Nodes Total Job Execution Time Excellent
PG Servers It's complicated… PG DB Size (each server treated separately) Good
PG Snapshot Snapshot size PG DB Size (each server treated separately) Good
PG Storage Actual DB Size Actual DB Size Excellent
s3 Total size of files by Client PG DB Size (across all Servers) Good

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Output: Cost by Business Activity
Data Storage
Processing
Results Storage
Archive & Backup
Deleted
Demo/
Test No recent Spots “Clones” Live
Collector
20% of our AWS cost is
for the daily processing of
clients whose latest spot is
more than 2 months ago
Clones cost
money too.
Archiving historic data
for live clients could
help reduce costs
Analysis of job
processing time will
help identify where to
target performance
improvements

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Efficiency Initiatives
• Pause Project: Cut processing on inactive customers
• Transparent to customers
• Reduce processing costs
• Reduce back-up costs
• Gross Margin: Identify customers with lower gross margin
• Archive: Move “cold” data to S3
• Reduce storage costs
• Improve DB performance
•Savings to date: 15%
•Next three months: 15%

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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Simon Treacy
streacy@amazon.com
@simon_cloud
Tom Duke
tom.duke@tvsquared.com

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