Powering the Intelligent Edge is one of the three pillars of Hewlett Packard Enterprise's corporate strategy. The session will cover HPE’s strategic direction and approach in the areas of IoT and data analytics. Join the discussion and learn how HPE’s solutions can help businesses prepare for the big data era.

1. © Copyright 2016 Hewlett-Packard Enterprise Corporation. The information contained herein is subject to change without notice.
Powering the Intelligent Edge
HPE – IoT Strategy & Direction
Robert C. Patterson Jr.
Chief Strategist
Data, Analytics & IoT
bob.patterson@hpe.com

2. Safe harbor
This presentation contains forward-looking statements that involve risks, uncertainties and assumptions, relating to, among other things, the planned separation. If
the risks or uncertainties ever materialize or the assumptions prove incorrect, the results of HPE may differ materially from those expressed or implied by such
forward-looking statements and assumptions. All statements other than statements of historical fact are statements that could be deemed forward-looking
statements, including but not limited to any statements of the plans, strategies and objectives of HPE for future operations, including the separation transaction; the
future performance of Hewlett-Packard Enterprise and HP Inc. if the separation is completed; the execution of restructuring plans and any resulting cost savings or
revenue or profitability improvements; any projections of revenue, margins, expenses, HPE's effective tax rate, net earnings, net earnings per share, cash flows,
benefit plan funding, share repurchases, currency exchange rates or other financial items; any projections of the amount, timing or impact of cost savings or
restructuring charges; any statements concerning the expected development, performance, market share or competitive performance relating to products or
services; any statements regarding current or future macroeconomic trends or events and the impact of those trends and events on HPE and its financial
performance; any statements regarding pending investigations, claims or disputes; any statements of expectation or belief; and any statements of assumptions
underlying any of the foregoing. Risks, uncertainties and assumptions include the need to address the many challenges facing HPE's businesses; the competitive
pressures faced by HPE's businesses; risks associated with executing HPE's strategy; the impact of macroeconomic and geopolitical trends and events; the need
to manage third-party suppliers and the distribution of HPE's products and services effectively; the protection of HPE's intellectual property assets, including
intellectual property licensed from third parties; risks associated with HPE's international operations; the development and transition of new products and services
and the enhancement of existing products and services to meet customer needs and respond to emerging technological trends; the execution and performance of
contracts by HPE and its suppliers, customers and partners; the hiring and retention of key employees; integration and other risks associated with business
combination and investment transactions; the execution, timing and results of the separation transaction or restructuring plans, including estimates and
assumptions related to the cost and the anticipated benefits of implementing the separation transaction and restructuring plans; the resolution of pending
investigations, claims and disputes; and other risks that are described in HPE's Annual Report on Form 10-K for the fiscal year ended October 31, 2013, and HPE's
other filings with the Securities and Exchange Commission, including HPE's Quarterly Report on Form 10-Q for the fiscal quarter ended July 31, 2014. As in prior
periods, the financial information set forth in this presentation, including tax-related items, reflects estimates based on information available at this time. While HPE
believes these estimates to be reasonable, these amounts could differ materially from actual reported amounts in HPE's Annual Report on Form 10-K for the fiscal
year ended October 31, 2014. HPE assumes no obligation and does not intend to update these forward-looking statements.

3. Agenda
• Business Drivers
• Approach
• Use Cases
• Q & A
3

4. 4
Digital signage
Telecom
infrastructure
Asset
tracking
Power distribution
Traffic lights
Security
Lighting
Air quality
Noise
Fire & emergency
Roadside equipment
Vibration
Access control
Electricity switch
Pumps
Product delivery
Driver behavior
Connected car
Product electronics &
entertainment
Vehicle leasing
Air conditioner
ePatient
management
Surveillance

6. 6
“Things” Data Characterized – Big Data is not just in the data center
− Can be Big Data, derived from
the physical analog world
− Is usually sourced from nature,
people, electrical and
mechanical devices,
environment, and objects
− Is mostly sensor acquired and
digitized via A/D conversion
“Things” Data

7. 2 petabytes
Walmart’s entire
transaction database
4 petabytes
Per-day posting to Facebook across 1.1
active billion users
in May 2016
40,000 petabytes per day
10m self-driving cars by 2020
Front camera
20mb / sec
Front ultrasonic
sensors
10kb / sec
Infrared camera
20mb / sec
Side ultrasonic sensors
100kb / sec
Front, rear and
top-view cameras
40mb / sec
Rear ultrasonic cameras
100kb / sec
Rear radar sensors
100kb / sec
Crash sensors
100kb / sec
Front radar sensors
100kb / sec
7
The amount of data we process is about to change — again

8. WW IoT Technology Investments - Reaching $1.29T TAM in 2020

9. Hybrid
IT
9
Built-in data analysis
& contextually aware
Beacons, sensors
and geo-positioning
Ubiquitous
connectivity
Reliable
performance
& experience
Adaptive trust
security
Mobile users,
apps and devices
Security &
resilience built-in Containerized, automated
and orchestrated
Intelligent
Edge
Your Apps
& Data
Driven by agile
DevOps
Flexible
consumption
Always
workload
optimized
Ecosystem of
innovation partners
Services

10. HPE Approach to IoT

11. 11
The EDGE: HPE puts the IT in IoT
#1
B
I
G
D
A
T
A
(IT: Data center / cloud)
New waves of
IoT data
moving
into data
centers and
clouds
New and optimized IT, moving
out the IoT edge
#2 IoT Engines
Long-term
learning
Enterprise
Systems &
Out-of-band Analytics
Operations &
In-band Analytics
IT
(OT: operations technology)
Real-time
actions
OT

12. Industrial IoT
(IIoT)
Internet of Things (IoT) – different perspectives on “things”
June 13, 2017 12
“thing” = Tractor/
Connected Car
“thing” = Machine “thing” = Cargo
“thing” = Vending Machine
“thing” = Truck
“thing” = Driver
“thing” = Snack “thing” = Consumer
“thing” = Product
“thing” = Worker
“thing” = Device
Consumer Product
Manufacturer

13. Industrial Internet of Things (IIoT) - OT
Instrumentation / Sensors
– Sensors
– Actuators
– Current loop
– HART
Controllers / Devices
– PLCs
– RTUs
– Specific, “fit for purpose”
Devices / Gateways
– Gateways / routers
– Aggregators
– Translators
– HART to Modbus TCP
Current loop, HART,
etc.
RFID Reader &
Antennas
RFID Tags
MAC Address
No MAC Address
PLC or RTU (bus
controller)
HART
RTU
HPE EL20 Wireless Gateway
HPE EL10 Wireless Gateway
13Wireless HART Gateway

14. The 4 Stage IoT Solutions Architecture:
14
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things”

15. 15
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things” Sensors/Actuators
(wired, wireless)
Stage 1
The 4 Stage IoT Solutions Architecture:
Process Control, SCADA and other OT Players
Process Control
– DCS
– Localized, distributed
– Quick update
– PID control
– PLCs
– Wireless HART
SCADA
– Remote
– Supervisory control
– RTUs
– Radio
Historian
– Historical database
– Some DCS systems
– DeltaV (Emerson)
– Simatic PCS 7 (Siemens)
– Experion (Honeywell)
– Some SCADA systems
– Telvent OASyS (Schneider)
– Wonderware (Schneider)
– CygNet (Weatherford)
– Some Historians
– OSIsoft PI
– Canary Labs

16. 16
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things” Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Stage 1 Stage 2
The 4 Stage IoT Solutions Architecture:

17. Communication media
17
– Wired Communication
– RS232, RS-485 serial communication
– Ethernet
– HART (Highway Addressable Remote Transducer)
– hybrid analog + digital protocol
– Can share the legacy 4-20 mA analog instrumentation
current loops
– Wireless Communications
– PAN
– Bluetooth / BLE (Bluetooth Smart)
– RFID – Readers, Tags, Applications
– ZigBee - Low power, self healing mesh for
communication
– LAN - WiFi (IEEE 802.11)
– WAN
– 3G, 4G and 5G
– LPWAN (LoRaWAN, NB-IoT)
– Satellite Communications (VSAT)
Security
– Layer 7 encrypted traffic
– Aruba ClearPass
– Which device gets access to the network?
– What policies apply?
– Niara
– What’s the device doing?
– Work with ClearPass to evict the suspect

18. Plenty of IoT Standards initiatives
ITU
The purpose of the International Telecommunication Union
(ITU) Global Standards for the Internet of Things is to provide
a visible single location for information on and development of
IoT standards, these being the detailed standards necessary
for IoT deployment and to give service providers the means to
offer the wide range of services expected from the IoT (Study
& research - concluded its activities in July 2015)
IPSO Alliance
The IPSO Alliance is not a standards organization, but an
alliance that promotes and supports Smart Objects, and
manages an IPSO Smart Object Registry. Develop,
establish, and create the industry leadership of an “IPSO
Platform” that includes the definition and support of Smart
Objects with an emphasis on object interoperability on
protocol and data layers and of Identity and Privacy
technologies.
IETF
The Internet Engineering Task Force (IETF) originally
focused on running IP over IEEE 802.15.4 radios and
has since then evolved into a much larger project,
covering IPv6 adaptation layer (6LoWPAN), the tree
routing layer (RPL), and finally an application protocol
substrate (COAP) – (Network & Protocols Focus)
7th Framework Programme/Horizon 2020
The EU commission Framework
Programmes for Research and Innovation on
the Internet of Things (PA & Smart Cities Focus)
3GPP
The 3rd Generation Partnership Project (3GPP)
unites telecommunications standard development
organizations to standardize cellular
telecommunications network technologies,
including radio access, the core transport network,
and service capabilities. Specifically to IoT, they
released the NB-IOT specification (Network
Focus)
IEEE
The mission of the Institute of Electrical and
Electronics Engineers (IEEE) IoT Initiative is to
serve as the gathering place for the global technical
community working on the Internet of Things
(Community & Best Practices Focus)
OIC/OCF
Unified as OCF in Feb 2016, it’s an entity whose
goal will be to help unify IoT standards so that
companies and developers can create IoT
solutions and devices that work seamlessly
together (Devices & Solutions
Interoperability Focus)

19. Partnership Project
Gets most of the telecom and industry
vertical standard bodies to work together and
interoperate in a standard way
Solves m2m/IOT fragmentation
Providing an overlaying standard on top of
the other standards and relevant initiatives,
with particular focus on interoperability.
Strong Participation
8 Standardization Organizations and 227
participating companies, including most
Tier#1 telecom operators and large
industries.
HPE Selected OneM2M as the reference architecture
“One standard to rule them all”
Proven maturity
Release specifications leverage on ETSI M2M
which was already quite mature & adopted by
many different industries like Telcos, Automotive,
Public Administrations, Energy/Utilities (market
proof)
Interoperability and bindings
Provides interoperability specifications to
support different IOT Standards initiatives
such as:
• LWM2M, OMA-DM, TR-069, BBF for device
management
Alignment with 3GPP MTC Interworking
Function (MTC-IWF): acts as the sole interface
for interworking 3GPP core network and M2M
service capability layer
Interoperability with OIC, IPSO, IETF
Defines oneM2M protocol bindings for many of
the common protocols such as CoAP, HTTP,
MQTT, WebSocket etc.
•
•
•

20. IoT On-Ramps
Power Line Twisted Pair RF BLE
v
Native Ethernet Native Wi-Fi
PHY And/Or Protocol Converters
Indoor
Uncontrolled
Spaces
Outdoor
Small Site
Cellular
BackhaulSwitchingConverged
Gateway IoT System

21. Pulling It All Together To Create A Trusted IoT
Vision
Meters
Contractors
Captive Portal
Role-Based Access
Access Rights
Access Control
Sensors
PLCs
Virtual AP 2
Guest
Meters
Vision
Sensors
PLCs
Contractors
Secure
Tunnel To
DMZ
RADIUS
LDAP
AD
NextGen Firewall
EMS/MDM/SIEM
ClearPass
Provisioning
DMZ
Niara
Analytics
Virtual AP 1
Control
© Copyright 2017. Aruba, a Hewlett Packard Enterprise Company. All rights reserved

22. 22
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things”
TheEdge
Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Stage 1 Stage 2
The 4 Stage IoT Solutions Architecture:

23. 23
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things”
TheEdge
Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Edge IT
(analytics, pre-
processing)
Stage 1 Stage 2 Stage 3
The 4 Stage IoT Solutions Architecture:

24. HPE Edgeline – The Full Portfolio
Entry
Intelligent Gateway
Performance
Intelligent Gateway
EL20
EL10
Entry
Converged Edge System
Performance Compute and Control
Entry Compute and Control
Performance
Converged Edge System
EL4000
EL1000
Compute at the edge, accelerate insight™
 Multiple functions in one box
 Open industry standards
 Environmentally ruggedized

25. HPE Moonshot & Edgeline – Different yet Common
25
Edgeline EL1000
Entry Converged Edge System
• 1 server cartridge
• Rack/wall/desk mount
• 2 standard PCIe slots
Edgeline EL4000
Performance Converged Edge System
• 4 server cartridges
• Standard 1U rack or wall mount
• 4 standard PCIe slots
Moonshot 1500
• 45 compute cartridges
• Standard 4.3U rack-mount
• Integrated NW Switch &
Systems Management
Common
Compute
Cartridges
Datacenter IT Edge IT or IoT
m510 m710x
Datacenter optimized
Dense, high bandwidth switching
Common
Software
Common
Management
Edge optimized
Temperature, shock, vibration hardened
HPE CONFIDENTIAL
Etc.

26. HPE Edgeline EL4000,EL1000 Expansion Options
26
Infiniband, More10Gb network, RAID controllers, Fiber Channel, PCIe accelerators…
HP IB FDR/EN 40 Gb 2P 544+ QSFP
Adapter
Infiniband
More10GbE
HP Smart Array P441/4GB FBWC
12Gb 2-ports Ext SAS Controller
RAIDcards
FPGA
Accelerators*
Nallatech 385 A Arria 10
/1150
Up to 2 PCIe Gen3x8 shared links for FH/HL cards
Up to 4 PCIe Gen3x8 links for FH/HL cards- 1 per cartridge
HP Ethernet 10Gb 2-port
560SFP+ Adapter
HP Ethernet 10Gb 2-port
561T Adapter
HP Ethernet 10Gb 2-port
546FLR-SFP+ Adapter
SFF HDD options for
additional storage
For Application
offload or bump-in-
the-wire processing
Moonshot
Cartridges
x2
Moonshot
Cartridges
x2
PCIe cards
x2 (FH, HL)
PCIe cards
x2 (FH, HL)
GPUs
NVIDIA Tesla P4
Inference at the Edge

27. HPE Edgeline EL4000,EL1000 I/O card options – Deep Data Ingest
27
PXI/PXIe card options. Converging scientific instrumentation and streaming analytics.
Multifunction
dataacquisition
Up to 2 PXI/PXIe Gen3x8 links – to Moonshot host
Up to 4 PXI/PXIe Gen3x8 links – configurable to one or
more hosts
Shared with permission from National Instruments
Reconfigurable
IO(FPGA)
Moonshot
Cartridges
x2
Moonshot
Cartridges
x2
PXI/PXIe
cards x2
PXI/PXIe
cards x2
And many more ….
LabView FPGA
– Edgeline systems are a new product category: Industry first
– Enable connectivity with previously isolated scientific equipment
– Have sufficient compute cores (Moonshot cartridges) for real-time
post-processing (condition monitoring, analytics)
– Example: 1 m510 cartridge links to 4 PXIe cards in EL4000. The remaining 3 cartridges run a
distributed analytics stack for live-processing acquired data. Enables real-time control.
And hundreds more ….

28. Modular Capability for Every Application
Multifunction I/O
FPGA
Digital I/O
Analog Input / Output
Vision and Motion
Counter / Timer / Clock
DAQ and Control
Oscilloscopes
High-Speed Digital I/O
Digital Multimeters
Signal Generators
Switching
RF Analyzers & Generators
Instruments
GPIB, USB, LAN
RS232 / RS485
CAN, LIN, FlexRay
Avionics Buses
I2C/SPI
Boundary Scan / JTAG
Interfaces
Power SuppliesSensor Measurements DeviceNet, PROFIBUS
Dynamic Signal Analyzers
Source Measurement UnitsSignal Conditioning
Reconfigurable I/O SCSI, Ethernet
VXI - VME
NI Offers 600+ PXI Products
And there are over 1500 PXI modules from 70+ vendors in the market today
Shared with permission from National Instruments

29. Building the Edgeline Ecosystem . . .

30. 30
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things”
TheEdge
Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Edge IT
(analytics, pre-
processing)
Stage 1 Stage 2 Stage 3
The 4 Stage IoT Solutions Architecture:

31. 31
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
Stage 1 Stage 2 Stage 3 Stage 4
The “Things”
TheEdge
Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Edge IT
(analytics, pre-
processing)
Data Center / Cloud
(analytics,
management, archive)
The 4 Stage IoT Solutions Architecture:

32. HPE’s Open Innovation approach to delivering next-gen
analytics for IoT
Data Lake
‒ In-memory analytics
‒ Optimized to run parallel with SAP
ERP apps to analyze real-time
operational and transactional data
‒ Deployment options include
appliance, managed cloud, or hybrid-
cloud service
‒ Advanced high performance analytics
‒ Optimized to analyze business/machine
data at petabyte scale
‒ Native integration with Spark & Kafka to
analyze streaming data in real-time
‒ Versatile deployment options
on-premise, native Hadoop, and cloud
‒ Index & analyze virtually any data type,
format or source
‒ Advanced enterprise search and
knowledge discovery
‒ Specialized machine learning functions
to analyze rich media including text,
audio, image, and steaming video
HPE Vertica HPE IDOL
Optimized with HPE Infrastructure
32

33. Enterprise-grade Hadoop: Industry Leading Hadoop Solution
33
Flexible high performance infrastructure
optimized for Hadoop & Spark
Enterprise grade software sold through
HPE
Robust Hadoop add-on solutions …
Paired with HPE Services to advise,
design & implement
HPE Differentiation
Infrastructure optimized for ALL
Hadoop, Spark & ecosystem tools
Higher density solutions and reduced
footprint vs. traditional Hadoop
offerings
Workload optimized solutions with
independently scalable storage and
compute for better performance and
lower TCO
+

34. HPE + Hortonworks BI Acceleration Solution
34
HPE Enterprise-Grade Hadoop
Standard and Elastic Editions
Hortonworks Data Platform 2.6
Big Data Solution For All Data Types
Hive LLAP
High Performance SQL Data Mart
Services
Strategy, Design, Implementation and
Support
Services
• HPE Pointnext, Hortonworks, and partner led options
Hortonworks Data Platform 2.6 with LLAP Benefits
• 100% Open Source allows you to evolve as technology evolves
• Faster Insights with up-to-date data to make better business
decisions and meet customer demand
• Scalable Architecture to deploy new business initiatives
HPE Enterprise-Grade Hadoop
• Compute/storage infrastructure options aligned to use needs and
growth trajectory

35. Deploying the Hadoop ecosystem at scale
Components can independently scale out for speed, volume, availability and variety of workloads
Real-Time
Batch
Collect /
Ingest
Store - Process - Manage Access
Edge Nodes Worker Nodes Application Nodes
HDFS M/R YARN
Security

36. Data Pipeline for Next Generation Analytics
36
One Platform for Real-Time Streaming, Interactive and Iterative Analytics

37. Multi-tenancy and big data
Evolution in containers and resource management
Containers in Hadoop Big data ON containers Hadoop on VMs
Core to the Hadoop 2.0
architecture
– Applications can run within
YARN containers and be
managed by Hadoop
Pioneered by the Amplab stack
on Mesos
– SMACK stack takes this
approach
– Several startups are offering
this model (BlueData, Mesos,
Robin Systems, etc.)
Pioneered by VMWare but central
to cloud deployments
– Data affinity enhancements
make this particularly
applicable to Hadoop
– Often how a shop will begin
experimenting with Hadoop
Platform
Hadoop with YARN containers
OS
MapReduce Spark HBase
Platform
Hadoop
OS Container
Platform
Hypervisor
OS OS OS
OS Container OS Container
Spark Cassandra
Hadoop Hadoop Hadoop
37

38. Big data as a service, elasticity and multi-tenancy
38
– Compute and storage separation
– Storage services (HDFS) on storage servers
– Compute services (YARN) on compute servers
Standard deployment
Hyperscale deployment
Two socket, 2U servers YARN Apps,
HDFS, Hbase
Storage-optimized servers
Compute-optimized servers
HDFS, Object
YARN Apps, Spark
SQL, Stream, Mlib,
Graph, NoSQL,
kafka, sqoop, flume
– Compute and storage separation
– Transparent access to multiple HDFS data stores
– Compute services virtualized on compute servers
– Multi-tenancy with Docker containers
Virtualizing with containers
Two socket, 2U servers
Storage-optimized servers
Compute-optimized servers
YARN Apps, Spark
SQL, Stream, Mlib,
Graph, NoSQL,
kafka, sqoop, flume
YARN Apps, Spark
SQL, Stream, Mlib,
Graph, NoSQL,
kafka, sqoop, flume
Tenant HDFS, or
Remote HDFS, NFS, Object
Tenant HDFS, or
Remote HDFS, NFS, Object
Tenant
Cluster
Tenant
Cluster
Tenant
Cluster
Tenant
Cluster
Tenant
Cluster
Tenant
Cluster
Enabled through orchestration and virtualization

39. Data Solutions Continuum
Modernize your traditional
data environments
Convergence
Unify the Data
Architectures
Generate Insights from Data
at any scale
Enterprise-grade Hadoop
Empower with SAP HANA
IoT Data Platform
Hybrid Data PlatformDeep Learning
Data Protection for SAP Object Storage
DB Consolidation
EDW Modernization IoT Applications
39

40. 40
Primarily
analog data
sources
Devices,
machines,
people, tools,
cars, animals,
clothes, toys,
environment,
buildings, etc.
The “Things”
Data Flow:
TheEdge
Sensors/Actuators
(wired, wireless)
Internet Gateways,
Data Acquisition
Systems
(data aggregation, A/D,
measurement, control)
Edge IT
(analytics, pre-
processing)
Data Center / Cloud
(analytics,
management, archive)
Stage 1 Stage 2 Stage 3 Stage 4
Visualization
Control Flow:
SW Stacks:
Analytics
Management
Control
Analytics
Management
Control
Analytics
Management
Control
The 4 Stage IoT Solutions Architecture:

41. IoT platform
Enable and manage
the IoT value chain
“Things” generate data and need control
HPE addresses IoT requirements
41
Security
End-to-end, proactive,
defense in depth
Services
Advise, transform,
integrate, manage,
analytics, governance
Control
Sense and respond, event management
Data
Contextual, insightful, at scale
Big data services, repositories and data lakes
Wi-FiLPWAN FixedCellular
Streaming, event processing, location and context awareness
Connectivity Ubiquitous, instant-on, reliable
Compute
Distributed, deep edge to the cloud
Ecosystem Open, extensive, partner-driven

42. Standardized
architecture
streamlines onboarding
new applications and
manages information in
a unified, secure, and
flexible end-to-end
system.
HPE Universal IoT Platform Overview
Network Interworking Proxy (NIP)
Device & Service
Management
(DSM) Data Acquisition
& Verification (DAV)
Data Analytics (DA)
Data Service Cloud (DSC)
BSS/OSS
Enterprise Specific Applications or Use Cases
CSP Network (Fixed / Mobile)Private Network (RF, LTN, Wi-Fi…)
IoT Devices, Sensors, and Connected Objects
IoT Gateways
IoT Network Core
A Horizontal Platform for Enabling Multi-Industry Vertical IoT Applications
SmartFarm
SmartFactory
SmartBuilding
SmartHome
SmartCity
ConnectedCar
FleetManagement
Safety&Security
NewUseCaseDevelopment
Use Case Enablement
13

43. Security: The Missing Link in IoT Devices
• Devices built for speed
and reliability may have
little, no, or outdated
security
• Many lack access rules,
key mgmt, zero day
attack prevention,
authentication, anti-
malware, encryption,
firewalls

44. HPE Security Solutions Overview
IoT Platform
IoT
Endpoints
Connectivity
Edge
Computing
Visualization
IoT Cloud /
Platform
HPE SecurityArcSight (Security Intelligence)
HPE Security Fortify (Application Security)
HPE Security – Data Security (Voltage/Atalla)
HPE Aruba (Communication Security)
Data,Applications,Communication,Users
44

45. Trusted Transformation approach
Assembling the ‘right’ Services and Technologies for IoT
– To manage risk of moving
to a future vision, HPE’s
Trusted Transformation
approach validates
alignment with business
priorities, and operational
and financial realities.
– Covers more than
technology- encompasses
people, processes, and
overall program
management.
End-to-end approach to manage the risk of the journey
Validated integration
& implementation
Program management &
sponsorship
Financial
options
Management of
change
Aligning people, process and technology change to enable the business
Strategy &
architecture
Validated
design
Support &
evolve
Transformation
workshop
Business Alignment | IT & OT Alignment | Investment Strategy | Training and certification
Validating technology & operations | Deployment experience | Decades of multi-vendor expertise
Proof of
Concept
45
Assessment &
analysis

46. IoT Colouring Book
46
Use Case Reference Architectures
i. Asset Management
ii. Location Enablement
iii. Condition/Inventory Monitoring
iv. Predictive Maintenance
v. IoT Application Platform
vi. Smart Meter Management
vii. Edge Compute incl. Edge-to-Core
Analytics
viii. Industrial IoT
ix. GE Predix
x. Real Time Drilling
xi. Converged Plant Infrastructure
xii. Security Horizontal Lifecycle
xiii. SAP HANA Solutions
xiv. Intelligent Spaces

47. Real Time Drilling Solution Reference Architecture
47
Use Case Description
Requirements Summary
Description
Monitor and predict real time (<1 sec) events in
remote locations with loss of connectivity to a
Core. This will lead to improvements in Non
Production Time (NPT), Health & Safety and
automated distribution of best practice via
Distance Deep learning
Top Requirements
1. Classification and prediction under 1 second
2. Ability to run intensive Edge Analytics with loss
of connectivity to Cloud
3. Ability to integrate data streams to/from SCADA
systems
4. Ability to integrate different approaches to
Analytics (deterministic, probabilistic, statistical,
machine learning, deep learning)
5. Enable multiple Edge locations to learn from the
experiences of another Edge location
Motivation Goals
– Enable real time insights in a remote location
– Avoid network issues where data analytics
engine is located n the Core and the ability to
guarantee real time insights is at risk or in some
cases simply not possible
– Avoid lost production
– Improve Health & Safety
– Reduce drilling campaign duration (Note Opex
costs are typically on hire / day rate basis in this
industry)
– Reduce cost of drilling (Capex & Opex)
– Develop an IoT architecture that can extend to
integrate adjacent use cases
Business Case Foundation
Minimize the impact of unforeseen events such as
an unexpected gas pocket or the drill string
becoming stuck. Such events can lead to serious
health risks and fatalities and add days to the
planned investment which directly drives cost up.
Cost benefits come from
– Avoided NPT
– Avoided unexpected Health & Safety events
– Achievement of drilling in the planned duration
and in some instances ahead of planned
duration thus reducing accumulated day rate
costs
Stakeholders
Well Engineering Managers who own the budgets
or Drilling. Drilling Supervisors responsible for
drilling in a specific location. Company men who
oversee multiple live drilling locations
Possible transformations
Enable Energy Companies to redesign their
organizational structures so scarce expertise can
be more effectively allocated to numerous drilling
locations at the same time (i.e. create new working
relationships between the remote location, onshore
control centers and corporate HQ, …

48. Real Time Drilling Solution Reference Architecture
Functional View
Develop
& Deploy
Management
Data
Applications
Communications
Location
DataOperations Management
Development &
Deployment
Developer view Operations View
Infrastructure Management
Communication Management
Infrastructure Automation
Compute
Resources
API view
Digital Oil Field
Solutions
Partner viewUser view
LAN Controller
Backup Data
WLAN Access
Unstructured
Data
Archive Data
Edge Analytics
Solutions
Location Solutions
3rd party solutions
Service Automation
Remote Operations (ILO)
Service Marketplace
Unreliable connectivity
Unreliable
connectivity
Unreliable
connectivity
Firewall
(WLAN Controller)
Firewall
Firewall
Gateway/
Messaging Bus
Storage
Resources
Access Policy
Manager
Structured
Data
Location Engine
Point
Wireless
Thing
Wired
ThingEDGE LOCATION

49. Comput
EdgeLine e
Resources
Storage
Resources
Access Policy
Manager
Real Time Drilling Solution Reference Architecture
Product Mapping
Dev
ent elop
& Deploy
Managemen
OpsBridge t
Data
API view User view
Applications
oC mmunications
Operations Management
Developm
& Deployment
Developer view Operations View
Infrastructure Management
Communication Management
InfrastructureAutomation
Digital Oil Field
Solutions
Partner view
LAN Controller
WLANAc
cess
EdgeAnalyt
Solutions
ics
3rd party solutions
Service Automation
Remote Operations (ILO)
Service Marketplace
Unreliable connectivity
Unreliable
connectivity
Fir
ewall
(WLAN Controller)
Firewall
Firewall
Aruba
Controller
HPE
Bespoke
Application
Open Group Compliant
Solutions
Druid / Distance
Deep Learning
HPE HPE Infra
OpsBridge Mgmt Suite
HPE HPE
OpsBridge OneView
HPE
IMC
HPE Aruba
IMC Air Wave
HPE HPE HPE Infra
OneView Mgmt Suite
HPE Helion HPE Helion
Dev Platform Stackato
HPE
Propel
HPE
CSA
Citrix VDI
SAP / Maximo
/ ERP
Aruba
ClearPass
Aruba
Controller
Aruba
Controller
Casandra
Location
Data
Backup Data
Unstructured
Data
Archive Data
Gateway/
Messaging Bus
Structured
Data
Location Engine
Node RedHPE Vertica
HPE
Hadoop
Historian
Kafka
HPE
EdgeLine
Intel
Windriver
Aruba NW
Infrastructure
Aruba
Access Point
Aruba ALE
Aruba ALE
Unreliable
connectivity
Point
Legacy SCADA
Wireless
Thing
Wired
ThingEDGE LOCATION
Location Solutions
Customize

50. HPE Global IoT Innovation Labs
Partner and customer engagement, interaction, assessment and
collaboration on solution development and applications
• Building, testing and pre-validation of
Edgeline systems and end-to-end HPE
IoT solutions
• Integrated real-life “Edge Experience
Zones“ to interact with and highlight
industry applications
• On-site access or secure remote access
• Datacenter connectivity and testing
Houston, Texas,
U.S.A.
Bangalore,
India
Singapore,
SE Asia
Geneva,
Switzerland
Houston Innovation Lab - https://www.hpe.com/us/en/servers/global-innovation-lab.html

51. IoT Use Cases – removed
due to customer sensitivity
- bob.patterson@hpe.com

52. Thank You!