Cloud Computing – Lecture 4

Function as a Service – Serverless

Computing2 March 2021

Chinmaya Dehury

chinmaya.dehury@ut.ee

1

Revisiting IaaS

In telecom, instead of paying for the

whole minute, you started paying for

seconds.

Instead of paying for the whole flat,

you started paying for one room and

other common shared area.

2

Revisiting IaaS

In telecom, instead of paying for the

whole minute, you started paying for

seconds.

Instead of paying for the whole flat,

you started paying for one room and

other common shared area.

Decreasing control over stack

implementation

Incre

asin

g fo

cu

s o

n b

usin

ess lo

gic

Physical Server

Virtual servers

Container

Can we apply this concept onto

IaaS cloud model ?

3

Serverless Computing

*img src: https://www.serverlesscomputing.org/wosc3/presentations/intro.pdf

4

Serverless Computing - Features

Abstraction of

servers

Event-driven

instant scale

Sub-second

billing

5

Serverless Computing• Newer workloads are a better fit for event driven programming

– Execute application logic in response to database triggers

– Execute app logic in response to sensor data

– Execute app logic in response to scheduled tasks etc.

• Applications are charged by compute time (millisecond) rather than by

reserved resources

• Greater linkage between cloud resources used and business operations

executed

• Serverless in a nutshell

– Event-action platforms to execute code in response to events

6

Serverless Computing

Develop, deploy and run your applications without taking care of the

underlined servers.

- Stateless computation

- Short running

- Event driven applications

- Auto-scaling (auto scale up & down)

- Charges for actual usage

7

Why serverless computing is so attractive ?

• Making app development

& ops dramatically faster,

cheaper, easier

• Drives infrastructure cost

savings

8

Source: Jason McGee, IBM; Serverless Conference 2017.

Vendors providing serverless computing

9

IBM- Cloud Functions

…and the most popular one is…

10/37Src: https://trends.google.com/trends/explore?date=today%205-

y&q=AWS%20Lambda,Cloudflare%20workers,Azure%20function,Google%20Cloud%20function,IBM%20Cloud%20Function

…and the most popular one is…

11/37Src: https://trends.google.com/trends/explore?date=today%205-y&q=%2Fg%2F11c0q_754d,%2Fg%2F11bw4c_dgq

FaaS in Public Clouds (1/4)

● AWS Lambda

○ Run code in AWS without managing infrastructure or software

○ Java, Go, PowerShell, Node.js, C#, Python, and Ruby code

○ Pricing is based on number of requests and GB-Sec "Memory-Duration"

○ Free: 1M requests a month. After: $0.20 per 1M

○ Free: 400,000 GB-Sec. After: $0.000017 per 1 GB-Sec

○ Some limit:

■ Function and layer storage (.zip file archives): 75GB,

■ Memory: upto 10GB

■ Concurrency: 1000 (can be increased)

■ Timeout: 900sec

12/37https://aws.amazon.com/about-aws/whats-new/2020/12/aws-lambda-supports-10gb-memory-6-vcpu-cores-lambda-functions/

FaaS in Public Clouds (2/4)

● IBM BlueMix Cloud Function○ IBM Bluemix, rebranded IBM Cloud in 2017

○ Based on OpenWhisk - Open Source Serverless cloud platform

○ Event, trigger & rule based execution

○ Supports any language*

○ Free: 400,000 GB-Sec. After: $0.000017 per 1 GB-Sec

○ Some limits:

■ Code size: 48MB

■ Memory: 128 MB -2GB

■ Concurrency: 1000

■ Timeout: 100ms – 600sec

13/37Src: https://cloud.ibm.com/docs/openwhisk?topic=openwhisk-limits

FaaS in Public Clouds (3/4)

● Google Cloud Function

○ Started with Google App Engine in 2008

○ JavaScript, Node.js, Python, Go

○ Pricing is based on number of requests GB-Sec, and GHz-sec

○ Free: 2M invocations, 400K GB-Sec, 200K GHz-Sec of compute time a month.

○ Some limits:

■ Code size: 100MB (compressed) for sources. 500MB (uncompressed) for sources plus

modules.

■ Memory: 4GB

■ Concurrency: 1000

■ Timeout: 540sec

■ HTTP request size per invocation: 10MB

14/37

FaaS in Public Clouds (4/4)

● Azure Functions

○ The Functions runtime is open-source.

○ C#, JavaScript, F#, Java, PowerShell, Python

○ Free: 1M invocations per month. After: $0.20 per million executions

○ Free: 400K GB-Sec pre month. After: $0.000016 per GB-Sec

○ Some limits:

■ Code size: no limit

■ Memory: 1.5 – 14 GBGB

■ Timeout: can be unbounded

■ Request size : 100MB

15/37Src: https://docs.microsoft.com/en-us/azure/azure-resource-manager/management/azure-subscription-service-limits#azure-functions-limits

Triggers, actions, rules (and packages)

16/37

Triggers, actions, rules (and packages)

● Services or data sources define the events they emit as triggers

● Event-programming model:

○ Runs code in response to events

○ Events (Trigger examples):

■ changes to database records

■ IoT sensor readings that exceed a certain temperature

■ new code commits to a GitHub repository

■ simple HTTP requests from web or mobile apps

○ Event can also trigger

■ multiple functions (parallel invocations)

17/37

Triggers, actions, rules (and packages)

18/37

Triggers, actions, rules (and packages)

● Action ○ a stateless function that executes arbitrary code.

○ small snippets of JavaScript or Swift code

○ custom binary code embedded in a Docker container● Instantly deployed and executed whenever a trigger fires

● Actions can be invoked

○ directly via REST API,

○ executed based on a trigger.

● The result of an action could also be a trigger of another function (sequential

invocations).

● A set of actions can be chained

19/37

Triggers, actions, rules (and packages)

● Asynchronous actions/invocation:

○ Here the invoker—caller request—does not expect a response

● Synchronously action/invocation:

○ Here the invoker expects a response as a result of the action execution.

20/37

Src:

https://docs.aws.amazon.com/lambda/latest/dg/images/invo

cation-sync.png

Src:

https://docs.aws.amazon.com/lambda/latest/dg/images/features-

async.png

Triggers, actions, rules (and packages)

21/37

# Hello world as an OpenWhisk action.

def myFunction(args):

name = args['name']

greeting = "Hello " + name + '!'

return {"greeting": greeting}

● Create an action:

wsk action create myfunction Hello-Python.py

● Invoke an action:

wsk action invoke myfunction --result --param name World

● Update an action:

wsk action update myfunction Hello-Python.py

Triggers, actions, rules (and packages)

22/37

Triggers, actions, rules (and packages)

● Developers associate the actions to handle the events via rules● Rule is an association of a trigger to an action

○ Many to many mapping

● Packages are a shared

collection of Triggers

and Actions

23/37

Triggers, actions, rules (and packages)

24/37

Event source Serverless Function

(Lambda Function)Services

• DB changed

• Request to endpoint

• Resource state changed

• Java

• Go

• Node.js

• C#

• Python

• etc

Some available frameworks…

● Apache OpenWhisk

● OpenFaaS

● Fn Project

25/37

Apache OpenWhisk

OpenWhisk is an event-driven compute platform also

referred to as Serverless computing or as a FaaS that runs

code in response to events or direct invocations.

● Initiated by IBM but now an Apache project

● Open source cloud platform

● Serverless deployment and operations model

● Optimal utilization and granular pricing

● Scales on a per-request basis

● Supports JS, Swift, Python, Java, Docker

26https://github.com/apache/incubator-openwhisk/blob/master/docs/about.md

Apache OpenWhisk - System overview

https://github.com/apache/incubator-openwhisk/blob/master/docs/about.md

27/37

Apache OpenWhisk - The internal flow of processing

https://github.com/apache/incubator-openwhisk/blob/master/docs/about.md

28/37

Apache OpenWhisk - The internal flow of processing

https://github.com/apache/incubator-openwhisk/blob/master/docs/about.md29/37

Nginx:

• exposes the public-facing HTTP(S) endpoint to the clients.

• Every request (even from wsk CLI) must go through this layer.

Controller

• Performs the authentication and authorization of every request.

• Responsible for the actual implementation of the OpenWhisk API.

• Decides the path that the request will eventually take

CouchDB

• To maintain the state of the system.

• The credentials, metadata, namespaces, and the definitions of

actions, triggers, and rules, are stored in CouchDB.

Consul

• Provides service discovery capabilities

• Allows controller to discover Invokers to invoke an Action.

Kafka

• Kafka buffers the messages sent by the Controller

Some available frameworks…

● Apache OpenWhisk

● OpenFaaS

● Fn Project

30/37

OpenFaaS Serverless framework

A framework for building serverless functions with Docker and Kubernetes that

offers first-class support for metrics.

● Ease of use through UI portal and one-click install

● Write services and functions in any language with Template Store or a

Dockerfile

● Build and ship your code in the Docker/OCI image format

● Portable: runs on existing hardware or public/private cloud by leveraging

Kubernetes

● CLI available with YAML format for templating and defining functions

● Auto-scales as demand increases including to zero

31/37

OpenFaaS Serverless framework

32/37Recommended

OpenFaaS workflow

33/37

• faas-netes : Kubernetes controller for OpenFaaS

• NATS provides asynchronous execution and queuing

• Prometheus provides metrics and enables auto-scaling through AlertManager

OpenFaaS – Templates

34/37

OpenFaaS vs OpenWhisk

35/37

OpenFaaS OpenWhisk

Popularity (Github Stars) 19K 5.2K

Contributors 156 184

Stability (Corporate Backer) VMWare IBM (Apache Foundation)

Development Language Go Scala

Packaging mechanism Docker container Docker container

Underlying technology Alertmanager / Prometheus,

Nats

CouchDB, Kafka, Nginx,

Redis, Zookeeper

Some available frameworks…

● Apache OpenWhisk

● OpenFaaS

● Fn Project

38/37

Fn Project

● Oracle announced the Fn project.

● Can be hosted on any cloud (private cloud, AWS, Oracle Cloud, Google

Cloud, and so on)

● Only requirement to run Fn is a Docker engine.

● Supported languages: Go, Java, Node.js, Python, Ruby

39/37

Fn Project

● Fn Project consists of 4 major components:○ Fn Server

■ Executes sync functions, returning responses to clients immediately

■ Executes async functions when capacity is available

○ The Fn Load Balancer

■ routes functions to certain nodes consistently for hot function efficiency

■ Scales each function independently

■ Can be used to scale Fn servers

○ Fn FDK’s:

■ Function Development Kits for popular languages - Java, Node.js, Python, Go, and Ruby

○ Fn Flow

40/37

Fn Project – Architecture

41/37

Fn Project – Deployment details

42/37

1. Builds container (multi-stage) + bumps version

2. Pushes container to registry

3. Creates/updates function & triggers

When you call the function via the call command or curl, the

container is executed and the results are returned to you.

Something different project…. Serverless Framework

● It is not a serverless platform, just a packaging mechanism.

● A software development kit for serverless.

● The package can be deployed on Lambda, Google Functions, Azure

Functions, Apache OpenWhisk, Cloudflare Workers, OpenFaas, Kubeless or

Fn

44

Advantages of Serverless/FaaS

● Very simple and "cheap" to scale

● Rapid prototyping

● Easy to modify serverless functions

● Pay only for the execution time, not for idle computation time

● Can create applications by composing functions written in different languages

45/37

Disadvantages of Serverless

● Harder to avoid vendor lock-in

○ Depend heavily on built in triggers and rules

● Lack of monitoring and debugging tools

● Composition and architecture complexity

● Slow cold-start

● What about stateful computations?

● Harder to predict costs

46/37

Common Serverless Application Use Cases

● Web applications

○ Static websites

○ Complex web apps

○ Packages for Flask and Express

● Backends○ Apps and services

○ Mobile

○ IoT

● Data processing○ Real time

○ MapReduce

○ Batch

○ Machine learning inference

47/37

● Chatbots

○ Powering chatbot logic

● Amazon Alexa○ Powering voice-enabled apps

○ Alexa Skills Kit

● IT automation○ Policy engines

○ Extending AWS services

○ Infrastructure management

Use Cases

48

Thomson Reuters

● Provides companies with the intelligence, technology, and human expertise

for quick decisions making.

● AWS Lambda allows Thomson Reuters to

○ load and process the streaming data

○ collects data from the Kinesis pipeline

○ loads data into the master dataset in Amazon S3

○ Lambda is triggered by Amazon S3 upon new data arrival

49

iRobot

● A global consumer robot company.

● Focusing on home-cleaning robot category

● iRobot is using about 25 AWS services.

● AWS Lambda and AWS IoT platform at the core

● AWS IoT platform : Provides the connectivity layer between the robots and

the iRobot cloud platform

● AWS Lambda runs code in response to events to provide function-based

compute services50

GreenQ

● Smart residential waste collection.

● Optimize every aspect of waste collection from scheduling and routing to

major fleet vehicle decisions and contract optimization.

● OpenWhisk serverless platform:

○ Collects information regarding every waste bin pickup

○ Every file and every MQTT message being uploaded to Bluemix triggers OpenWhisk actions

51

Serverless is Good and Not Good for…

● Short-running

● Stateless

● Event-driven

- Microservices

- Mobile Backends

- Bots

- IoT

- Service Integration

- Stream processing

53/37

● Long-running

● Stateful

● Number crunching

- Databases

- DL training

- Heavy-duty stream analytics

- Numeric simulation

- Video streaming

What next ???

54

Let’s move to lab session…

55

References● https://github.com/apache/incubator-openwhisk/blob/master/docs/about.md

● https://cloud.google.com/functions/docs/writing

● https://cloud.google.com/functions/pricing#compute_time

● https://docs.microsoft.com/en-us/azure/azure-functions/supported-languages

● https://azure.microsoft.com/en-us/pricing/details/functions/

● https://github.com/Azure/azure-functions-host

● https://docs.microsoft.com/en-us/azure/azure-functions/functions-overview

● https://www.serverlesscomputing.org/wosc3/presentations/intro.pdf

● http://deck.fnproject.io/

● https://netflix.github.io/titus/

● https://www.youtube.com/watch?v=66PxX3oGVCA

● https://aws.amazon.com/solutions/case-studies/thomson-reuters/

● https://aws.amazon.com/solutions/case-studies/irobot/

● https://thenewstack.io/behind-scenes-apache-openwhisk-serverless-platform/

● Dehury, Chinmaya, et al. "Data Pipeline Architecture for Serverless Platform." European Conference

on Software Architecture. Springer, Cham, 2020. 56

57

Thank you