Edge architecture ieee international conference on cloud engineering

Netflix’s Global Cloud
Edge Architecture
Mikey Cohen mikey@netflix.com
Edge Engineering Platform
Netflix

Over 44 million subscribers
in over 40 countries

Netflix accounts for over 30% of peak internet traffic
in North America

One billion hours ~
100,000 years
per month...

Netflix supports over 1000 device types

Edge Services
● Front door to Netflix
● Edge Routing - Zuul
● API - Edge Server
● Playback services

How does Netflix Streaming work?*
* A simplified view

How does Netflix Streaming work?
Netflix
Services
in Amazon Cloud
Your CE Device
CDN

Device Under the Hood
Netflix
Services
in Amazon Cloud
Your CE Device
CDN
User Interface
Netflix Streaming Platform
DRM encodingCE integration

User Interface loaded, data retrieved from
Netflix Edge Service
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services

User Interface Loaded
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services

Movie Authorization
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services
Authorize

Obtaining License
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services
License

Movie starts streaming
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services
PlayData

Movie starts streaming
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services

Periodic “bookmark” calls note place in
movie
User Interface
DRM
Netflix
Services
in Amazon Cloud
encoding
Your CE Device
CDN
CE integration
Edge
Services
bookmark

Edge Services - What we are talking about
today
User Interface
DRM encoding
Your CE Device
CDN
CE integration
bookmarkNetflix
Services
in Amazon Cloud Edge Services

Edge’s lofty mission
● High Availability
● Good performance
● Data broker between many services and
devices in a global, high volume, rapidly
innovating, highly dynamic service
● Clients and services are constantly changing

Edge stats
● Billions of incoming requests per day
○ Over 10X outgoing service calls per request
● About 10 device changes per day
● Daily service pushes
● Daily routing changes

Architecture Goals
● Infrastructure
○ Availability
○ Resiliency
○ Scalability
● Application
○ Platform diversity
○ Rapid innovation
○ A/B Testing
● Delivery
○ Automation
○ Insights

Netflix’s Global Cloud Architecture

High Level Regional Edge Architecture
ELB
Edge Service
Netflix Services
ELB
Playback Service
ELB
Zuul
Website Service

Zuul
ELB
Edge Service
Netflix Services
ELB
Playback Service
ELB
Zuul
Website Service

What is Zuul?
● Open source framework for dynamically
reading, writing, and executing filters that act on
incoming HTTP requests
● Dynamically compiled filters written in Groovy
○ Any JVM language supported
● Filters share state through a request scoped
context

How we use Zuul
● Authentication
● Insights
● Stress Testing
● Canary Testing
● Dynamic Routing
● Service Migration
● Load Shedding
● Security
● Static Response handling
● Active/Active traffic management

Zuul Filter Characteristics
● Type
● Execution Order
● Criteria
● Action

Zuul Filter Lifecycle
HTTP Request
"pre" filters "routing" filter(s) "post" filters
Origin Server
"custom" filters
Http Request Http Response
"error" filters

zuul filter lifecycle
HTTP Request
"pre" filters "routing" filter(s) "post" filters
Origin Server
"custom" filters
Http Request Http Response
"error" filters

Example Filter
File: DeviceDelayFilter.groovy
1 class DeviceDelayFilter extends ZuulFilter {
2
3 def static Random rand = new Random()
4 @Override
5 String filterType() {
6 return 'pre'
7 }
8
9 @Override
10 int filterOrder() {
11 return 5
12 }
13
14 @Override
15 boolean shouldFilter() {
16 return RequestContext.getRequest().
17 getParameter("deviceType")?equals("BrokenDevice"):false
18 }
19
20 @Override
21 Object run() {
22 sleep(rand.nextInt(20000)) //Sleep for a random number of seconds between [0-20]
23 }
24 }

Multiple Active Regions
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
Services

Multiple Active Regions - NM vs GE
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

Multiple Active Regions- Cassandra Replication across regions
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

DNS Misrouting
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

Geo lookup resolves IP in west
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS
GEO

Zuul east routes to Zuul west
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS
GEO

Response is from west
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS
GEO

Regional Failure
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

Catastrophe in US-East
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

East Coast is Down
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

Switch DNS to point to US-West
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

East traffic flows to West
ZUUL
API
Cassandra
Services
ZUUL
API
Cassandra
ServicesDNS
DNS

The Edge Service - Netflix’s API Tier
ELB
Edge Service
Netflix Services
ELB
Playback Service
ELB
Zuul
Website Service

What’s wrong with REST for Netflix?

REST
● One Size Fits all
● One Data Format Fits All
● REST tends to be atomic
● Average 25 REST requests to build up a
page.

Netflix’s Groovy Scripting Layer

Edge Scripting Tier
● Device teams write scripts for their device
○ control content, format, endpoints
● Code injected directly into Edge Service at
runtime
○ Scripts are in production in about 30 seconds

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Async Service Layer API
Hystrix (Fault tolerance)Endpoint
Manager
JVM
Netflix Services
Edge Server Architecture

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
Pushing a Script
UI Engineer
/ps3/home
script

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
Controller pulls new script / compiles
UI Engineer
/ps3/home
script

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
Script Activated
UI Engineer
Activate

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
Service Layer

Purpose of the Service Layer
● Interface to business logic (our API)
● Shield data consumers from service
changes
● Combine and expose business data in a
logical and consistent manner
● All Service Layer methods are async using
RxJava
○ Hides concurrency and underlying implementation

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
RxJava

RxJava
● Why?
○ How do you expose an async service as an API?
○ Solution to compose async flows and sequences of
data
○ Rich set of operators to filter and interact with data

How RxJava Helps
● Need to hide concurrency from script writers
○ Minimize the “bad things” consumers of our API on
box can do.
○ Hide the internal implementation
■ Change concurrency of any given call
■ Switch to non-blocking IO

Endpoint
Code
(Groovy)
Endpoint Controller
RxJava
Manager
JVM
Netflix Services
Hystrix

How Hystrix helps
● Latency and Fault Tolerance
○ Stop cascading failures. Fallbacks and graceful degradation. Fail fast and rapid recovery.
○ Thread and semaphore isolation with circuit breakers.
● Realtime Operations
○ Realtime monitoring and configuration changes. Watch service and property changes take effect
immediately as they spread across a fleet.
○ Be alerted, make decisions, affect change and see results in seconds.
● Concurrency
○ Parallel execution. Concurrency aware request caching. Automated batching through request collapsing.

Edge Delivery
● Continuous deployment
● Automated system integrity analysis
● Tools for facilitating delivery

ZUUL ZUUL-CANARY
ZUUL-DEBUG ZUUL-SQUEEZE
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Edge Cluster Organization

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Most Requests to Main Origin

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Some requests to Canary

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Response Validation

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Fork response to Main and Canary

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Validate response
Validate
response
integrity

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Targeted Debugging

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
SQUEEZE
ORIGIN
ELB
Squeezing the Origin

ZUUL ZUUL-CANARY
MAIN
ORIGIN
CANARY
ORIGIN
DEBUG
ORIGIN
ELB
Finding service Capacity
SQUEEZE
ORIGIN

Scryer - Predictive auto-scaling
● Why?
○ Reactive doesn’t work in all cases
○ Reacting is sometimes too late
■ Sunday morning cartoons
○ Reactive overreacts
■ Superbowl, World Cup, Outages
■ Fixed size scaling
○ All in All - more reliable and saves money

How does Scryer work?
● Traffic shape analysis
○ Monday vs Monday
○ Sunday vs Sunday, etc
○ FFT based smoothing

Accounting for regular spikey traffic

Other Scryer Factors
● Traffic volume analysis
○ At least 4 weeks of data
○ Linear regression based on time of day
○ Correct the prediction based on today’s trend.
● Instance factors
○ Instance startup time
○ Instance capacity (obtained by squeeze testing)
● Scale (up/down) actions scheduled based on prediction

Future - Large Projects on Edge
● Async, non-blocking servers
● Service layer redesign
● Internal Insights
● Global Insights

Edge Architecture Today
ELB
API Service
Netflix Services
ELB
Streaming
Service
ELB
Zuul
Website Service
Zuul Zuul

Future Edge Architecture
ELB
API/ Edge Service
Netflix Services
Playback Services
ELB
Zuul
Website

Global Insights
API/ Edge
Service
Netflix
Services
Playback Services
Zuul
User
Interface
Insight
EngineEvent Stream
Client
Data

Netflix in the Cloud -
5 years later
Lessons learned

● Code failure - Continuous delivery
● Service failure - fallbacks and redundancy
● Instances and Zone failure - redundancy
● Cloud infrastructure failure - Multiple active regions
● Human failure - Automation
Building for Failure

Drawbacks of the cloud
● Some failures are difficult to detect the cause
○ Huge variability in instance performance that are
almost impossible to explain.
○ Network barriers
○ Multi tenancy
○ Firewalls
● Very limited access to information/ ability to fix issues

Software focus: Cloud’s greatest strength
● Scale our business
● Automate processes
● Radically experiment
● Remain resilient
● Move quickly

Netflix Culture - Our secret sauce
● Freedom and responsibility
● Highly aligned teams
● Aversion to process
● Design for necessity
● Design for failure
● Engineering teams operating their services

Netflix OSS
● Zuul - Smart edge router
● RxJava - Functional reactive libraries
● Hystrix - SOA resiliency
● + a lot more!

For more Info on Netflix Cloud Technology:
Read our Technology Blog : http://techblog.netflix.com/
Check out our Open Source Cloud Projects : http://netflix.github.io

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