The 2018 Public Cloud Performance Benchmark Report measures global network performance of the three big public cloud providers - AWS, GCP and Azure. Find out: a) The architectural differences among the three clouds providers b) The good, bad and ugly of global network performance and the reason behind regional variations c) Inter-AZ and Inter-Region performance comparisons d) How the clouds connect with each other and if multi-cloud is ready for prime time

2. @ThousandEyes
• Public Cloud Performance Benchmark
Report
– Overview
– Methodology
• Benchmark Findings
• Summary and Best Practice
Recommendations
• Q&A
Agenda
Archana Kesavan
Sr. Product Marketing Manager
@archana_k7

3. @ThousandEyes
About ThousandEyes
Network Intelligence solution
that helps you understand
performance from every user
to every application over any
network
User App
Enterprise, Endpoint and Cloud Agents
Routing
End-to-End Performance Data
User
Experience
App
Performance
Routing
Topology
Network
Topology
Network
Connectivity
Device
Performance
18/20
top SaaS
companies
5/6
top US banks
50+
Fortune 500
companies
8/10
top global
software
companies

4. @ThousandEyes
Understand Experience for Any User and App
Lightweight software-based agents
easily installed on your own network,
in data centers, branch offices & VPCs.
Enterprise Agent Endpoint Agent
Browser-based plugins
installed on end-user
laptops and desktops.
End User ExperienceInternal Vantage Points
Cloud Agent
Globally distributed agents installed
and managed by ThousandEyes in
150+ POPs around the world.
External Vantage Points

5. @ThousandEyes
HOW DO WE MAKE DECISIONS
ABOUT THE CLOUD TODAY?

6. @ThousandEyes
Cloud Decision Factors
Global PresenceGlobal Presence Pricing
$
Services
Pricing
$
Services Performance
Cloud
Decisions
?
Global PresenceGlobal Presence Pricing
$
Services
Pricing
$
Services Performance
Cloud
Decisions

7. @ThousandEyes
What’s Been Missing?
No network architecture
No loss or jitter
No predictability analysis
Single provider
Lack comparisons
Limited geos
No multi-cloud
Snapshots not trends
DEPTH
TIME
BREADTH

8. • Industry’s first vendor-
neutral, data-based
research
• Empowering data-driven
decisions in the cloud
PUBLIC CLOUDPERFORMANCEBENCHMARKREPORT
2018 Edition

9. @ThousandEyes
Data Methodology and Scope
END USER
MEASUREMENTS
INTER-AZ
MEASUREMENTS
INTER-REGION
MEASUREMENTS
MULTI-CLOUD
MEASUREMENTS
Pre-deployed
Active
Monitoring
Platform
Global Vantage
Points
Path
Visualization
Bi-directional Network Metrics
THOUSANDEYES AGENT THOUSANDEYES AGENT

10. @ThousandEyes
End User Measurements
27 Global Locations
2515 1555 Regions

11. @ThousandEyes
Inter-AZ Measurements
A B
C
Region X
A B
C
Region X
A B
C
Region X

12. @ThousandEyes
Inter-Region Measurements
15 REGIONS25 REGIONS15 REGIONS

13. @ThousandEyes
Multi-Cloud Measurements
15 REGIONS
25 REGIONS
15 REGIONS

14. @ThousandEyes
Data-Driven Decision Framework
30 DAYS 10 MIN-INTERVALS
40M Unique Measurements
Loss Latency
80M Unique Measurements
Jitter
120M Unique Measurements
Network Path
160M Unique Data Points
20M
Unique
Measurements
40M
Unique
Measurements

15. @thousandeyes #cloudstate18 © 2018 ThousandEyes. All Rights Reserved.
GOOD BAD UGLY

16. @ThousandEyes
How Fast Can You Go Within Your Cloud?
< 2 ms
E X P E C TAT I O N
0.82 ms
eu-west-2
sa-east-1
us-east-1
ap-south-1
0.61 ms
1.13 ms
0.92 ms
0.72 ms
1.05 ms
centralus 1.05 ms
0.79 ms
southamerica-east1
europe-west-2
asia-south-1
asia-southeast1
0.60 ms
0.84 ms
0.92 ms
0.93 ms
< 2 ms
EXPECTATION
Inter-AZ performance is reliable and consistent
Indicates robust regional backbone for redundant multi-AZ architectures

17. @ThousandEyes
■ 10% faster than Baseline ■ Baseline ■ 10%–30% slower than Baseline ■ 30% slower than Baseline
How to Choose Inter-Region Pairs?
Region Pair
Bidirectional Latencies (ms) from Sydney, Australia (Primary Region)
AWS Azure GCP
Tokyo 109.59 107.32 104.34
Singapore 174.65 108.36 168.49
Mumbai 228.48 162.15 228.92
Region Pair
Bi-directional Latencies (ms) from Sydney, Australia (Primary Region)
AWS Azure GCP
Tokyo 109.59 107.32 104.34
Singapore 174.65 108.36 168.49
Mumbai 228.48 162.15 228.92

18. @ThousandEyes
Leverage Data for Inter-Region Decisions
Get the report for complete inter-region data
www.thousandeyes.com

19. @ThousandEyes
• Comparable performance in North America and Western Europe
• No significant packet loss or outages in a 4-week period
Where is End User Performance Strong?
HOSTING REGION: VIRGINA, VA
BI-DIRECTIONAL LATENCIES
HOSTING REGION: UNITED KINGDOM
BI-DIRECTIONAL LATENCIES
■AWS ■Azure ■GCP
Asia Europe North America Oceania South America
USER LOCATION
0
50
100
150
200
250
300
350
400
ms
Asia Europe North America Oceania South America
USER LOCATION
0
50
100
150
200
250
300
350
400
ms ■AWS ■Azure ■GCP

20. @ThousandEyes
But, Asia Has A Different Story

21. @ThousandEyes
GCP is 3X Slower
450
400
350
300
250
200
150
100
50
0
HOSTING REGION: MUMBAI, INDIA
BI-DIRECTIONAL LATENCIES
■ AWS ■ Azure ■ GCP
USER LOCATION
GCP has 3x the
network latency
ms

22. @ThousandEyes
Why is GCP 3x Slower to Asia?
Users in Europe take
a sub-optimal route
to India
9
36
1
1
1
Traffic moves through the
GCP backbone through
the United States

23. @ThousandEyes
No direct route from Europe to India
Why is GCP 3x Slower to Asia?

24. @ThousandEyes
AWS Mumbai Performance Anomaly
Considerably higher
latency variation
with AWS
450
400
350
300
250
200
150
100
50
0
HOSTING REGION: MUMBAI, INDIA
BI-DIRECTIONAL LATENCIES
■ AWS ■ Azure ■ GCPms
USER LOCATION

25. @ThousandEyes
AWS in Asia is Less Predictable
Hosting Region: Tokyo
Hosting Region: Singapore
Hosting Region: Mumbai
50 ms 100 ms 150 ms0
BI-DIRECTIONAL LATENCY VARIATIONS
■ AWS ■ Azure ■ GCP

26. @ThousandEyes
HOW DO YOU GET PAST
PERFORMANCE VARIATIONS?

27. @ThousandEyes
Is Multi-Cloud a Safe Alternative?
Negligible packet loss and jitter
AWS, Azure and
GCP directly peer
with each other
Traffic between clouds
does not leave their
backbones
The BIG 3 play nice!
Consider Multi-Cloud for best of breed choices

28. @ThousandEyes
WHY IS AWS SO MUCH LESS
PREDICTABLE IN ASIA?

29. @ThousandEyes
Why AWS Exhibits Less Predictability
Singapore
DATA CENTER
Frankfurt
USER
Internet
HongKong
Frankfurt
Internet
Frankfurt
Internet
Not all clouds are created equal
Architectural and connectivity differences have an impact on performance

30. @ThousandEyes
4
6
12
1
1
AWS Loves the Internet
Traffic from users across
the globe traverse the
Internet longer on AWS
deployments
Traffic from Singapore
enters the AWS backbone
in Dallas, Texas

31. @ThousandEyes
Azure and GCP Network Differently
9
12
2 6
1
2 1 1 7
Traffic from Singapore
enters the MSFT backbone
in Singapore

32. @ThousandEyes
AWS Address Performance Instability With New Service
• AWS Global Accelerator for
improved performance
• Pay AWS more $$ to ride
their internal backbone
instead of the Internet
• Cloud Trend Alert:
Monetization of the provider
backbone https://blog.thousandeyes.com/aws-addresses-
performance-instability-with-aws-global-accelerator/

33. @ThousandEyes
Summary of Learnings
NOT ALL CLOUDS ARE CREATED EQUAL
Architectural and connectivity differences have an impact
on performance
GEOGRAPHICAL PERFORMANCE VARIES
Do not assume uniform performance across the globe and
use data to test, validate and optimize
INTRA-CLOUD PERFORMANCE IS STELLAR
Robust internal backbone supports redundant and
distributed application architectures
MULTI-CLOUD IS SAFE
Symbiotic coexistence between the Big 3 and stable
multi-cloud performance
AWS has less performance
stability in Asia
GCP is 3x slower from
Europe to India
Inter-AZ performance
between 0.5 ms – 1.5 ms
AWS, Azure and GCP peer
directly with each other

34. @ThousandEyes
Recommendations
qUse data, not your gut to guide your cloud investment decisions
qFind your “best” and ”worst” region pairs before deciding on your
cloud architecture
qConsider your organization’s tolerance to Internet exposure and
evaluate risk vs $$
qFactor in inter-AZ and inter-region performance. Application
performance and user-experience rely on it
qTrust, but verify. Avoid assumptions in the cloud

35. @ThousandEyes
Implement a Cloud Readiness Lifecycle
Baseline
performance
and use data
to guide cloud
decisions
READY
Monitor continuously as there is
no steady state in the cloud
OPERATE
Monitor
performance to
clouds, within
clouds and
between clouds
DEPLOY

36. @ThousandEyes
Stay In the Loop on Public Cloud Research
For more PCPBR information, please go to
https://www.thousandeyes.com/research/public-cloud
@archana_k7
@thousandeyes
Webinars, blogs, reports
Email us for a “customized” briefing

37. @ThousandEyes
Questions?
Q & A
DOWNLOAD THE REPORT AT www.thousandeyes.com