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Ben Eichorst, Cloud Architect

8/1/17

AWS Greengrass

What is AWS IoT

Why Greengrass?

What is Greengrass?

Problems solved by Greengrass

Key concepts

Technical features of AWS Greengrass

Law of physics

Law of economics

Law of the land

Value of processing data at the source

What is AWS Greengrass?

AWS Greengrass extends AWS onto your devices, so they can act locally on the data they generate,

while still taking advantage of the cloud.

Data processed

in the cloud

Data processed

locally

Moving to the edge

AWS Greengrass

Localactions

Local

Lambda Functions

Security

AWS-grade

security

Localtriggers

Local

Message Broker

Data and state sync

Local

Device Shadows

Features

AWS Greengrass

Respond quickly

to local events

Operate

offline

Simplified device

programming

Reduce the cost of

IoT applications

AWS-grade

security

Benefits

AWS Greengrass

Who is AWS Greengrass for?

Mining

Energy

Agriculture

Communication

Construction

Consumer

electronics

Manufacturing

Finance, insurance,

and more…

Automotive

Medical

Partner ecosystem

Operating

systems

Consulting

Wireless

operator

OEM

ISVs

Silicon

Launch customers and partners

AWS

Snowball Edge

Problems solved by Greengrass

Problem Solution

Impact

Rio Tinto has connectivity

challenges at some of the mine

locations where large,

expensive machinery is in play.

Rio was looking for a way to

still leverage the cloud to

predict failures and learn from

their environment.

Rio is using GG to calculate road roughness

from sensors on haul trucks and create

an online heat map of the rough roads.

Maintenance crews will use this information

to effect road repairs and reduce premature

damage of their machinery.

Unlike current on-premise programs for monitoring the machinery, GG allows

for real-time alerts and machine-to-machine communication while leveraging

Machine Learning models in the cloud when connectivity is available.

Problem Solution

Impact

Nokia has seen a need in

industrial IoT to analyze

video streams at the edge

and send the data to remote

centers only when anomalies

are detected.

Deploying Greengrass on Nokia Multi-access

Edge Computing platform and combining

it with Nokia private mobile network solutions.

This joint solution will make it possible for the

oil industry to pair real time drilling data with

production data of nearby wells.

Due to the cost of bandwidth being expensive, this allows Nokia to optimize the

data that is sent to other wells and to the cloud based on rules and alerts set up on

the locally-processed data.

Problem Solution

Impact

Pentair provides beer and

water filtration systems to large

industrial brewing customers

like Heineken. Most of their

industrial customers are

located in remote geographies

with unreliable internet

connections. They also have

customers who do not want to

open up firewalls port for

internet connectivity.

They want to phase out or integrate their

current PLCs with GG clusters to make real-

time decisions on premise and eventually

streaming to the cloud for further analysis.

Pentair can take this use case and replicate it across their various workloads in

commercial, residential and industrial spaces. Taking the cloud models and, when

needed, pushing them into local environments.

Problem Solution

Impact

As Konecranes specializes in

the manufacturing and

service of cranes globally,

they discovered that when they

needed to make updates to

their machinery it meant

downtime and local presence

onsite.

Using Greengrass has enabled them to

deploy updates using cloud models that

continually get smarter over time as they

sync with the local environments.

This allows them to simplify their current crane architecture and make it possible to

update calculations to the cranes in a secure way even after the installation has

taken place.

Problem Solution

Impact

Stanley Black and Decker finds

it unsustainable to ingest,

transmit, store, query and

analyze all data generated at

the edge and more specifically

on construction sites or rural

areas with constrained network

resources.

Green Grass from AWS enables Stanley

Black and Decker to monitor and filter data at

the edge of the network enabling applications

to send asset health and predict any

mechanical failures before they occur. Edge-

based applications built on Greengrass will

help us detect and compare vibrations

emitted by high value tools to historical

signatures that indicate everything from

normal operations to imminent failure.

Instead of trying to use all the data Stanley Black and Decker will utilize Greengrass

allows us to focus on the right data. Some of the applications we see this fit includes

remote troubleshooting of hydraulic assets by technicians, maintenance interval

tracking, fuel savings, and alerts.

Key concepts

Greengrass components

Greengrass is software, not

hardware (you bring your own)

2 components that work

together:

• Greengrass Core

• IoT Device SDK

AWS Greengrass Core (GGC)

The runtime responsible for

Lambda execution, messaging,

device shadows, security, and

for interacting directly with the

cloud

AWS Greengrass Core (GGC)

• Min single-core 1 GHz• Min 128 MB RAM• x86 and ARM• Linux (Ubuntu or Amazon)

• The sky is the limit

IoT device SDK

Any device that uses the IoTdevice SDK can be configured to interact with AWS Greengrasscore via the local network

Devices can be small or big

Starts with the IoT device SDK for C++, more coming soon

Devices work together locally

An AWS Greengrass groupis a set of cores and other devices configured to communicate with one another

Devices work together with the cloud

AWS Greengrass works with AWS IoT to maintain long-lived connections and process data via the rules engine

Your Lambda functions can also interact directly with other AWS services

AWS Greengrass pricing

Active Devices Price per Device

3

3–10,000

10,000+

Free for 1 year

$0.16/month$1.49/year

Call us

Technical features of AWS Greengrass

Local Lambda

Lambda functions are event-driven compute functions

With AWS Greengrass you can write Lambda functions in the cloud and deploy them locally

Local Lambda

AWS Greengrass runs Lambda functions written in Python 2.7

Invoke Lambda functions with messaging and shadow updates

Local Lambda—what you can do

Command and control

Offline operation

Data filtering and aggregation

Get smarter over time

Shadows

JSON documents that represent state of your devices and Lambda functions

Define them however is logical to you—a car, an engine, a fleet

Sync to the cloud or keep them local

Shadows—what you can do

Device state (current and desired)

Granular device state (only synched to the cloud for debug)

Lightweight configuration

Messaging

Local MQTT pub/sub messaging

Define subscriptions between publishers and subscribers

Apply MQTT topic filters

Security

Mutual auth, both locally and also with the cloud

Certificate on your devices can be associated to SigV4 credentials in the cloud

You can directly call any AWS service from AWS Greengrass

AWS Greengrass