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Fog Computing for Robotics and Industrial Automation
Paul Pop, Professor of Cyber-Physical SystemsDTU Compute, Technical University of Denmark
Ph.D. inComputer Systems
Linköping UniversitySweden
Associate professorDTU Compute,Technical Universityof Denmark (DTU)
Assistant professorLinköping University Sweden
ProfessorDTU Compute,
Technical Universityof Denmark (DTU)
Director
2
Industry 4.0
Digitization of the manufacturing sector, with embedded sensors in virtually all product components and manufacturing equipment, ubiquitous cyberphysical systems, and analysis of all relevant data.
Definition
Benefits $15Trillionincrease in global GDPwith Industrial internetover the next 20 years
Source: McKinsey
3
“Software is eating the world”
IT has transformed our society. Everything is now software in the Cloud.
What do all of these have in common?
Will the factorymove the the Cloud?
4
The automation pyramid and OT
The Automation Pyramid:
Soon to be Ancient History?
22
Rigid infrastructure with
separation between levels
of functionality
Levels connected by dedicated,
specialist networks
Data exchange only via gateways
or proprietary systems
Difficulties to transparantly
access data at the cyber
pyramid (machine) level
Operationstechnology (OT)
The hardware and software dedicated to detecting or causing changes in physical processes through direct monitoring and/or control of physical devices such as valves, pumps, etc.
Source: TTTech
5
We cannot move the factory to the Cloud because of OT
High dependability, long lifetime, safety-criticalHard real-time
Tough requirements
Reboot? Retry? Restart?
6
What is Cloud that comes close to the ground (edge of the network)?
System-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from Cloud to Things.Similarities: (Dependable Real-Time) Edge Computing
© Nebbiolo Technologies 6
The IoT Infrastructure and Fog Computing
Public'Clouds' Private'Clouds'
Enterprise)Data)Center)
Machines,'devices,'sensors,'actuators,'things'
Tradi7onal'ICT'end9points'
Public'and'Private'Communica7ons'Infrastructure''
(Internet,'Enterprise'Ethernet,'WiFi)'
Compu7ng'and'Storage'Today:'
1. Clouds'2. Enterprise'Datacenters'3. Tradi7 onal'and'Embedded'
Endpoints'
IoT'requires'more'virtualized,'scalable,'reliable,'secure,'real97me'capable'Compu7ng'
and'Storage'at'the'Edge:'
Fog'Compu7ng!''
What is Fog Computing?
Source: Flavio Bonomi
7
Industry 4.0 = Convergence of IT and OT
Information Technologies:
Virtualization, Big Data, Automation Analytics, Scalability, SDN, Security and Privacy
Operations Technologies:
Real-time, Safety, Reliability, Control, Machine Connectivity and Data Acquisition, Human Machine Interface
IT
OT
Cloud
Fog
IoT Endpoints
8
Vision: OT becomes virtualized
9
A history of Fog
FlavioBonomi
Sept 2010
Bay area
Source: Flavio Bonomi
April 2016 Hannover Messe
KUKA calls it Real-Time Edge Computing
10
Fog: Converged Functionality for Industrial Automation
Cell 1 Cell N
Machine Vision and Bar Code Reader
PLC System
Robotics ControllerAnd Visualization
Cell Controller
Manufacturing Execution System
ERP/MRP System
Adding Functionality
Nebbiolo Fog Node
Data Center/Clouds
Converging Functionality
Source: Flavio Bonomi
11
Fog Computing: a new Infrastructure layer
1. Communications, gateway networking convergence
2. Edge data management, analytics
3. Distributed application hosting
4. Virtualization of all resources, multi-tenancy
5. Security and Privacy
6. Real-time, local control
7. Scalability
8. Reliability
Cloud
Fog
IoT Endpoints
12
Fog: Hardware convergence
47
Industrial Micro-controller
PLC
Industrial PC
Real-time, High-availability, Industrial Fog Node
Virtualizing and consolidating , multiple PLCs and Industrial PCs
(SBCs)
3 Slot System:=
20-30 Industrial PCs
3 Slot System:=
~15-20 IoT Gateways
IoT Gateway
Data center Servers or
Micro-Servers
13
Fog: Virtualization and Security
A combination of physical separation (multicore), hard, RT-NRT Virtual Board/Machine based virtualization and more lightweight Linux Container based virtualization
GuestOS+Hypervisor (KVM) or Thin Hypervisor (Wind River, Lynx,..)
Linux RT-OS 2Windows RT-OS 1…
RT-AppsRT-AppsWin-Apps
NRT-VM 2 RT-VM 2RT-VM 1
LXC
1
LXC
N…
AAA
LXC: Linux Container
Virtualization
Decentralized model, including distributed authentication, trusted booting, secure software management, SDN based connectivity control, OpenVPN, NFV Security…
Security
14
Network convergence: IEEE Time-Sensitive Networking
IntegrationMultiple traffic classes share the network, supporting applications with mixed-criticality requirements
Separation: Virtual links separate different criticalities
Principles
Fog
IoTEndpoints
DeterministicNetworking
Standardization as IEEE TSN
19
A number of IEEE 802.1 standards due for release early in 2017
15
Open standards will win: IEEE 802.1 TSN and OPC UA over TSN
Creating a Flexible IoT Infrastructure
with Deterministic Ethernet
24
OPC UA over TSN: one unified
architecture for all communication
infrastructure elements
Logical machine/control
boundaries are dissolved
Direct access to machine
data from ERP/MES
Real-time and non-real-time
domains integrated
OPC UA provides leading
technology of proven security
concepts
Learning from the Internet
Experience: Digital Platforms
and Open Innovation win. Source: TTTech
16
Software convergence: an app store for the factory
17
Safety: Certification is very expensive
For safety-critical systems, decision-makers require pre-release safety assurance evidence that it manages risk acceptably.
Highly critical systems require certificationby a regulatory authority.
Verification
System
System and Safety Requirements
Argumentation Environment
Evidence
Safety standard
Documents
Company Independent safety assessor
Certificate
Source: Hans Hansson
18
The industry is not the new “space customer”
Components are produced for computers and consumer electronics; they have 3-6 year production lives and narrow operating temperature ranges
The needs of the “space customer” are not addressed
Industry is not prepared to pay ”space customer” prices
Source: Boeing
Design
Production
Airplanes:
Service
Computers: Design
Production
Service
System archictecture
Software
Mfg. processes
Piece parts
0 10 20 30 40
Time, years
Technology element:
19
Security
Security Framework 2: Motivation
IIC:PUB:G4:V1.0:PB:20160926 - 14 -
critical infrastructure. With a geographically distributed IIoT system, care must be taken to ensure that disruption of an isolated system does not cascade to have global effects.
Organizations must take these risks seriously; they must use their expertise to make their IIoT systems trustworthy. The use of sensors and actuators in an industrial environment is not the typical Information Technology (IT) experience, nor are systems that span many organizations and organizational systems. IT and OT prioritize system characteristics differently. For example, resilience in IT is less important than in OT, and security is less important in OT than in IT, as illustrated in Figure 2-1. These characteristics interact with each other, and can conflict. In IIoT systems, these system characteristics must converge and be reconciled with each other into overall system trustworthiness.
Figure 2-1: Convergence of IT and OT Trustworthiness
IIoT organizations must place increased importance on safety and resilience beyond the levels expected in many traditional IT environments. IIoT systems may also have data flows that include intermediaries and involve multiple organizations, requiring more sophisticated security approaches than, for example, link encryption. Unfortunately, IT departments rarely speak the same language as those concerned with control systems and OT. The two perceive risk differently, and they cannot be combined for positive gain without a balanced consideration of their differing motivations.
The highest priority of many OT systems is safety: do not cause injury or death, do not put public at risk and protect the environment from harm. The second and third priorities are often quality of production and meeting production targets, which depend on the reliability and resilience of the system. Reliability and resilience are required to prevent the interruption of society-critical processes such as the electric grid, and to avoid idling machinery that represents large investments in physical infrastructure. Security aspects are considered in OT, but given that most systems are not connected it is mostly physical security. (Some industries, such as healthcare, must protect patient data.) Security concepts such as user-based access control applies less often in OT systems than they do in IT.
20
The Cloud will come to the factory
44 45Hello Industrie 4.0 we go digital
Economic, social, technological and ecological megatrends such
as globalization, urbanization, digitization and sustainability are
radically changing the industrial landscape of the future. This poses
new challenges for manufacturing industry. Industry is responding
to these processes of change and to the increasing competitive
pressure with shorter product cycles and a more differentiated
product portfolio.
Versatile solutions are required to cushion workload peaks and
resource bottlenecks in the age of Industrie 4.0. Matrix production
may become a decisive competitive factor through configurable
production cells, the transfer of parts and tools using automated
guided vehicles (AGVs) and the separation of logistics from
production.
Matrix productionHighly flexible production is becoming reality.
The production cell for easy conversion.
Four robots handle and join the parts. Tool maga-
zines in the cells enable provision of the required
tools. This enables adaptation of the cell during
the cycle time.
Cloud
Fog
High risk of change, but!
High impact, and new equipment needed only for about about 40 to 50% of installed base
Source: McKinsey
21
Industry 4.0 vision: Research at DTU Compute
ConfigurationtoolsResource
management
FogMiddleware
OPCUA/DDS
HypervisorpartitionedOS
FogApp
VirtualizedControl
Pla
tfo
rm
FOGNODE
Se
curi
ty,
Saf
ety
and
De
pe
nd
abil
ity
Computation
ESR14ESR13
ESR
12ES
R11
ESR8
ESR7
ESR2
ESR1
ESR3
ESR4
ESR5
Storage Communication
FogApp
FogApp
API
API
API
ESR6
ESR10 ESR9
Dataanalytics
WP1FogComputingPlatform
WP2
Res
ourc
eM
anag
em
ent
and
Mid
dlew
are
WP3DependabilityServicesandApplicationModeling
AppAppApp
ESR15
FORA proposal: 15 PhDsFog Computing for Robotics and Industrial Automation
DTU’s IoT Center, iotcenter.dk
We are open to researchers from other departments at DTU, other Danish universities, and we’re welcoming company members.
Prof. Paul Pop. Industrial IoTProf. Nicola Dragoni. IoT Security and Privacy Prof. Jens Sparsø and Assoc. Prof. Martin Schoeberl. IoT Platforms Assoc. Prof. Michael S. Berger. IoT Communications Infrastructure Prof. Henrik Madsen. IoT Cyber-Physical Modeling and Smart Energy IoTProf. Lars Kai Hansen’ group. IoT Data Analytics Prof. Jan Madsen. Health IoTAssoc. Prof. Sven Karlsson. IoT System Software
Mission: Performing research on IoT technologies and facilitating collaboration among researchers and practitioners in Denmark.
Vision: Dependable and secure IoT enables efficient solutions to societal challenges.
23
Thank you!
1.Machine
4.Enterprise
3.Manufacturing
2.Control
EnterpriseManu-
facturing
Machine Control
IT+OTIT
OT
IT-OTgap crossedwithmulti-disciplinary,inter-sectoral,internationalResearch