Software Architecture in the age of Cloud Computing

SOFTWARE
ARCHITECTURE
IN THE AGE OF
CLOUD
COMPUTING
JAROSLAV GERGIC
Industrial Keynote
16th European Conference on Software
Architecture (ECSA), Prague,
19 – 23 September 2022

AG E N DA
INTRODUCTION
CLOUD SCALE COMPUTING
ARCHITECTING CLOUS SCALE SAAS
CLOSING THOUGHTS
SUMMARY

JA RO S L AV
G E RG I C
Always busy building the next big thing,
now living in the confluence of
cybersecurity, machine learning,
and cloud computing.
2022  1995:
Cisco, GoodData, Ariba, IBM Research, Reuters, Mobil
Server, LCS International
Mentoring: StartupYard, JIC, MSIC
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C LO U D
C O M P U T I N G
LET’S DEFINE THE TERM
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PUBLIC CLOUDS IN
CLOUD
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E N T E R P R I S E
S C A L E
is no longer the
summit of software
architecture
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LET’S TALK CLOUD SCALE
Cloud Computing =/= Public Cloud
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LET’S TALK CLOUD SCALE
Software as a Service (SaaS)
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B2C
Serve millions or billions of users
• Facebook
• YouTube
• TikTok
• Seznam.cz
B2B
(tens of) thousands businesses
• Salesforce
• Dropbox*)
• WorkDay
• GoodData*)
H OW B I G I S C LO U D S C A L E ?
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B 2 B S A A S : Cloud scale is at lest
three orders of
magnitude bigger than
enterprise scale.
Because you need to serve
thousands of enterprises.
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Reverse Migration
• Both Dropbox and GoodData started
originally on AWS
• As they grew, they sought to reduce costs
• GoodData migrated to Rackspace
managed hosting in 2014
• Dropbox migrated to their own datacenters
in 2016
B2B
(tens of) thousands businesses
• Salesforce
• Dropbox*)
• WorkDay
• GoodData*)
P U B L I C C LO U D V S P R I VAT E D C
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Public Cloud
• developer productivity
• time to market
• smaller scale
• high-margin product
Private Datacenter
• operational costs
• steady state product
• extreme scale
• margins under pressure
P U B L I C C LO U D V S P R I VAT E D C
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C LO U D S C A L E
S A A S
A RC H I T E C T U R E
WHAT DOES IT TAKE TO ARCHITECT CLOUD
SCALE SOFTWARE AS A SERVICE?
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Scalability Costs SLAs
Security
Compliance
Productivity
A RC H I T E C T I N G C LO U D S A A S
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S C A L A B I L I T Y
• Horizontal scaling
• Distributed computing
• Redundancy and Fault Tolerance
• Elastic workloads
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SINGLE CAUSE OF FAILURE
(vs. Single Point of Failure)
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S I N G L E C AU S E O F FA I LU R E
• DNS issue
• credentials rotation
• kernel update
• networking issue
• Infrastructure-level configuration change
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Beware of ubiquitous things, which seemingly always work fine!

AVO I D I N G T H E P I T FA L L S
• avoid singletons at any cost*)
• always think of blast radius when any
component, service or piece of underlying
infrastructure fails
• pro tip: checkout out service mesh such as
ISTIO (https://istio.io/)
• allows us to operate multiple
interconnected K8S clusters
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*) there can be only one!

C A PAC I T Y
P L A N N I N G
Why would I need to
do capacity planning in
a public cloud?
Is not it elastic by
design?
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C O S T S
• Gross Margin in SaaS
• Gross Margin = (Revenue – COGS)/Revenue
• COGS – Cost of Goods Sold
• What is COGS in SaaS?
• All costs needed to operate your SaaS offering.
• HW, SW, operations, support
• What is the benchmark Gross Margin in SaaS?
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20
80%

C O S T S
• Gross Margin in SaaS
• Gross Margin = (Revenue – COGS)/Revenue
• COGS – Cost of Goods Sold
• What is COGS in SaaS?
• All costs needed to operate your SaaS offering.
• HW, SW, operations, support
• What is the benchmark Gross Margin in SaaS?
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80%
COGS
$10
$2

C O S T S AV I N G S
STORIES FROM THE TRENCHES
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L I N U X K E R N E L T U N I N G
Low-level Linux kernel settings
like huge pages and NUMA
options settings led to
35% - 40% performance boost
for the prevailing workloads
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R E G U L A R E X P R E S S I O N S 1 0 1
Parsing input data at cloud
scale…
On multiple occasions we hit
performance issues with 3rd
party regex libraries in different
programming languages.
The improvement was > 10x.
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E L A S T I C S C A L I N G W I T H S P OT
I N S TA N C E S
Use case:
• A stateful compute and memory
intensive workload driven by
incoming telemetry flow.
Solution:
• Fleet of inexpensive spot instances
coupled with ML-based capacity
predictor.
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S L A S
• SLAs – Service Level Agreements
• Uptime, Latency, Throughput
• Recovery Time/Point Objectives (RTO/RPO)
• Requires supporting infrastructure
• Monitoring – metrics, dashboards
• Logging – instrumentation,
troubleshooting, auditing
• Alerting – 24/7 reliable notification with
duty rotation and escalation paths
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S E C U R I T Y & C O M P L I A N C E
Security Compliance
Threat Modeling SOC 2, ISO 27001, HIPAA, GDPR, Accessibility
Vulnerability Management SOC – Security and Organization Controls
Access Controls SOC 2 - Security, Availability, Processing
Integrity, Confidentiality, or Privacy
Supply chain attack prevention Objectives -> Controls -> Assessments
Security Monitoring PII protection
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~30% of R&D effort

( D E V E LO P E R ) P RO D U C T I V I T Y
• Continuous Integration / Continuous
Delivery pipelines (CI/CD)
• Development, Testing and Release
Processes
• Quality Assurance, Cycle Time
• Making sure the above scale to many
R&D teams – avoiding bottlenecks.
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Scalability
•Horizontal
scaling
Distributed
computing
Redundancy
and Fault
Tolerance
Elastic
workloads
Costs
Gross Margin
Profiling
Performance
Tuning
Cost
Optimization
Capacity
Management
SLAs
Uptime
Latency
Throughput
RTO / RPO
Monitoring
Logging
Alerting
Security
Compliance
Threat
Modeling
Vulnerability
Management
Supply Chain
Management
Security
Monitoring
SOC2, ISO
27001
GDPR, PII
Productivity
•CI / CD
Development
Testing
Release
DevOps
Scalability
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W H AT N E X T ?
NOW, WHEN I AM DONE ARCHITECTING AND
BUILDING MY CLOUD SCALE SAAS OFFERING?
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E VO LV I N G : P U B L I C C LO U D
• Periodically review and benchmark new
instance types.
• Review, evaluate and benchmark new
services provided by the vendor.
• Issue recommendations and develop
blueprints for R&D teams.
• Plan migration.
• Rinse & Repeat.
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E VO LV I N G : P R I VAT E D C
• Periodically perform capacity planning and
maintain HW order book based on up-to-date
predictions.
• Periodically review and benchmark new HW
generations. Negotiate prices with the
vendor(s).
• Issue recommendations and develop
blueprints for R&D teams.
• Plan migration.
• Rinse & Repeat.
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S TA R T M E U P !
PRODUCTION WORKLOADS IN PUBLIC CLOUD
NEWCOMER GUIDE 2022 EDITION
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W H E R E TO S TA R T ?
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https://googlecloudcheatsheet.withgoogle.com/

P U B L I C C LO U D S I N 2 0 2 2
• All three leading public cloud providers
(AWS, Azure, GCP) exhibit increasing
complexity.
• It is relatively easy to spin up proof of
concepts or play with technologies.
• But launching production workloads is a
whole different story. It is not just about
getting started, but also about doing
things right.
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S O F T W A R E A R C H I T E C T U R E
I N T H E A G E O F C L O U D
C O M P U T I N G
The cloud-era software architect needs to accommodate not only
functional requirements and customer-defined throughput and
performance requirements at cloud scale, but also a large set of non-
functional requirements related to cyber security, compliance, developer
productivity, and most notably also the financial/cost characteristics,
which at cloud scale can make or break a software-as-a-service
company. The role of a software architect thus became interdisciplinary
by nature and its mental model needs to accommodate all the above
non-functional aspects all the while maintain full picture across all
layers of the software stack from user-facing features all the way down
to operating system and underlying hardware platform levels.
Interdisciplinary “decathlon”
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O U T LO O K
S IM P LIFICATION
There are vast opportunities for simplification
and codification of best practices as the cloud
computing industry matures.
HYBRID CLOUD
Hybrid cloud deployments will become more
prevalent to protect margins.
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QUESTIONS?
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T H A N K YO U
Jaroslav Gergic
@jgergic jaroslavgergic
https://cognitive.cisco.com/
https://conf.researchr.org/home/ecsa-2022
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Software Architecture in the age of Cloud Computing

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