What's covered: - What AWS IoT is - Why Greengrass? - What is Greengrass? - Problems solved by Greengrass Key concepts - Technical features of AWS Greengrass

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Ben Eichorst, Cloud Architect
8/1/17
AWS Greengrass

3. What is AWS IoT
Why Greengrass?
What is Greengrass?
Problems solved by Greengrass
Key concepts
Technical features of AWS Greengrass

5. Law of
physics
Law of
economics
Law of
the land
Value of processing data at the source

7. What is
AWS Greengrass?

8. 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

9. Local
actions
Local
Lambda Functions
Security
AWS-grade
security
Local
triggers
Local
Message Broker
Data and
state sync
Local
Device Shadows
Features
AWS Greengrass

10. Respond quickly
to local events
Operate
offline
Simplified device
programming
Reduce the cost of
IoT applications
AWS-grade
security
Benefits
AWS Greengrass

11. Who is AWS Greengrass for?
Mining
Energy
Agriculture
Communication
Construction
Consumer
electronics
Manufacturing
Finance, insurance,
and more…
Automotive
Medical

12. Partner ecosystem
Operating
systems
Consulting
Wireless
operator
OEM
ISVs
Silicon

13. Launch customers and partners
AWS
Snowball Edge

14. Problems solved
by Greengrass

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. Key concepts

21. Greengrass components
Greengrass is software, not
hardware (you bring your own)
2 components that work together:
• Greengrass Core
• IoT Device SDK

22. AWS Greengrass Core (GGC)
The runtime responsible for
Lambda execution, messaging,
device shadows, security, and for
interacting directly with the cloud

23. 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

24. IoT device SDK
Any device that uses the IoT device
SDK can be configured to interact
with AWS Greengrass core via the
local network
Devices can be small or big
Starts with the IoT device SDK
for C++, more coming soon

25. Devices work together locally
An AWS Greengrass group
is a set of cores and other
devices configured to
communicate with one another

26. 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

27. 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

28. Technical features
of AWS Greengrass

29. Local Lambda
Lambda functions are event-driven
compute functions
With AWS Greengrass you can
write Lambda functions in the
cloud and deploy them locally

30. Local Lambda
AWS Greengrass runs Lambda
functions written in Python 2.7
Invoke Lambda functions with
messaging and shadow updates

31. Local Lambda—what you can do
Command and control
Offline operation
Data filtering and aggregation
Get smarter over time

32. 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

33. Shadows—what you can do
Device state (current and desired)
Granular device state (only synched
to the cloud for debug)
Lightweight configuration

34. Messaging
Local MQTT pub/sub messaging
Define subscriptions between
publishers and subscribers
Apply MQTT topic filters

35. 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

36. AWS Greengrass