Internet DRAFT - draft-kunze-coin-industrial-use-cases

draft-kunze-coin-industrial-use-cases







COINRG                                                          I. Kunze
Internet-Draft                                                 K. Wehrle
Intended status: Informational                    RWTH Aachen University
Expires: September 10, 2020                               March 09, 2020


             Industrial Use Cases for In-Network Computing
                draft-kunze-coin-industrial-use-cases-02

Abstract

   Cyber-physical systems and the Industrial Internet of Things are
   characterized by diverse sets of requirements which can hardly be
   satisfied using standard networking technology.  One example are
   latency-critical computations which become increasingly complex and
   are consequently outsourced to more powerful cloud platforms for
   feasibility reasons.  The intrinsic physical propagation delay to
   these remote sites can already be too high for given requirements.
   The challenge is to develop techniques that bring together these
   requirements.  Utilizing available computational capabilities within
   the network for in-network computing concepts can be a solution to
   this challenge.  This document discusses selected industrial use
   cases to demonstrate how in-network computing concepts can be applied
   to the industrial domain and to point out essential requirements of
   industrial applications.

Status of This Memo

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Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  In-Network Control / Time-sensitive applications  . . . . . .   4
     2.1.  Characterization and Requirements . . . . . . . . . . . .   4
       2.1.1.  Approaches  . . . . . . . . . . . . . . . . . . . . .   5
   3.  Large Volume Applications/ Traffic Filtering  . . . . . . . .   6
     3.1.  Characterization and Requirements . . . . . . . . . . . .   6
     3.2.  Approaches  . . . . . . . . . . . . . . . . . . . . . . .   7
       3.2.1.  Traffic Filters . . . . . . . . . . . . . . . . . . .   7
       3.2.2.  In-Network (Pre-)Processing . . . . . . . . . . . . .   8
   4.  Industrial Safety (Dead Man's Switch) . . . . . . . . . . . .   9
     4.1.  Characterization and Requirements . . . . . . . . . . . .   9
       4.1.1.  Approaches  . . . . . . . . . . . . . . . . . . . . .   9
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   7.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  10
   8.  Informative References  . . . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   The Internet is based on a best-effort network that provides limited
   guarantees regarding the timely and successful transmission of
   packets.  This design-choice is suitable for general Internet-based
   applications, but specialized industrial applications demand a number
   of strict performance guarantees, e.g., regarding real-time
   capabilities, which cannot be provided over regular best-effort
   networks.

   Enhancements to the standard Ethernet such as Time-Sensitive-
   Networking [TSN] try to achieve the requirements on the link layer by
   statically reserving shares of the bandwidth.  These concepts are
   well-suited for industrial settings with well understood
   communication patterns where the communication paths are encapsulated
   at the factory sites.  In the Industrial Internet of Things (IIoT),
   however, more and more parts of the industrial production domain are
   interconnected.  This increases the complexity of the industrial



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   networks, makes them more dynamic, and creates more diverse sets of
   requirements.  Furthermore, process control is imagined to be
   exercised from remote clouds for feasibility reasons which is why
   solutions on the link layer alone are not sufficient in these
   scenarios.

   Common components of the IIoT can be divided into three categories as
   illustrated in Figure 1.  Following
   [I-D.mcbride-edge-data-discovery-overview], EDGE DEVICES, such as
   sensors and actuators, constitute the boundary between the physical
   and digital world.  They communicate the current state of the
   physical world to the digital world by transmitting sensor data or
   let the digital world interact with the physical world by executing
   actions after receiving (simple) control information.  The processing
   of the sensor data and the creation of the control information is
   done on COMPUTING DEVICES.  They range from small-powered controllers
   close to the EDGE DEVICES, to more powerful edge or remote clouds in
   larger distances.  The connection between the EDGE and COMPUTING
   DEVICES is established by NETWORKING DEVICES.  In the industrial
   domain, they range from standard devices, e.g., typical Ethernet
   switches, which can interconnect all Ethernet-capable hosts, to
   proprietary equipment with proprietary protocols only supporting
   hosts of specific vendors.

   The challenge is to develop concepts that can include off-premise
   entities (such as distant cloud platforms) as well as proprietary

    --------
    |Sensor| ------------|              ~~~~~~~~~~~~      ------------
    --------       -------------        { Internet } --- |Remote Cloud|
       .           |Access Point|---    ~~~~~~~~~~~~      ------------
    --------       -------------   |          |
    |Sensor| ----|        |        |          |
    --------     |        |       --------    |
       .         |        |       |Switch| ----------------------
       .         |        |       --------                       |
       .         |        |                   ------------       |
    ----------   |        |----------------- | Controller |      |
    |Actuator| ------------                   ------------       |
    ----------   |    --------                            ------------
       .         |----|Switch|---------------------------| Edge Cloud |
    ----------        --------                            ------------
    |Actuator|  ---------|
    ----------

   |-----------|       |------------------|     |-------------------|
    EDGE DEVICES        NETWORKING DEVICES        COMPUTING DEVICES
     Figure 1: Industrial networks show a high level of heterogeneity.



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   hosts into the communication and still satisfy the performance
   requirements of modern industrial networks.  The in-network computing
   paradigm presents a promising starting point because (pre-)processing
   data within the network can speed up the communication, e.g., by
   reducing the amount of transmitted data and thus congestion.
   Flexibly distributing the computation tasks across the network helps
   to manage dynamic changes.  Specifying general requirements for the
   different application scenarios is difficult due to the mentioned
   diversity.  This draft characterizes and analyzes three distinct
   scenarios to showcase potential requirements for the industrial
   production domain and to illustrate how in-network computations can
   be helpful.

2.  In-Network Control / Time-sensitive applications

   The control of physical processes and components of a production line
   is essential for the growing automation of production and ideally
   allows for a consistent quality level.  Traditionally, the control
   has been exercised by control software running on programmable logic
   controllers (PLCs) located directly next to the controlled process or
   component.  This approach is best-suited for settings with a simple
   model that is focussed on a single or few controlled components.

   Modern production lines and shop floors are characterized by an
   increasing amount of involved devices and sensors, a growing level of
   dependency between the different components, and more complex control
   models.  A centralized control is desirable to manage the large
   amount of available information which often has to be pre-processed
   or aggregated with other information before it can be used.  PLCs are
   not designed for this array of tasks and computations could
   theoretically be moved to more powerful devices.  These devices are
   no longer close to the controlled objects and induce additional
   latency.

   It is worthwhile to investigate whether the outsourcing of control
   functionality to distant computation platforms is viable because
   these platforms have a high level of flexibility and scalability.  In
   the following, we describe the requirements and characteristics of
   the control setting in more detail.

2.1.  Characterization and Requirements

   A control process consists of two main components as illustrated in
   Figure 2: a system under control and a controller.  In feedback
   control, the current state of the system is monitored, e.g., using
   sensors and the controller influences the system based on the
   difference between the current and the reference state to keep it
   close to this reference state.



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    reference
      state      ------------        --------    Output
   ---------->  | Controller | ---> | System | ---------->
              ^  ------------        --------       |
              |                                     |
              |   observed state                    |
              |                    ---------        |
               -------------------| Sensors | <-----
                                   ---------
            Figure 2: Simple feedback control model.

   Apart from the control model, the quality of the control primarily
   depends on the timely reception of the sensor feedback, because the
   controller can only react if it is notified of changes in the system
   state.  Depending on the dynamics of the controlled system, the
   control can be subject to tight latency constraints, often in the
   single-digit millisecond range.  While low latencies are essential,
   there is an even greater need for stable and deterministic levels of
   latency, because controllers can generally cope with different levels
   of latency, if they are designed for them, but they are significantly
   challenged by dynamically changing or unstable latencies.  The
   unpredictable latency of the Internet exemplifies this problem if
   off-premise cloud platforms are included.

   The main requirements for the industrial control scenario are low and
   stable latencies to ensure that processes can work continuously and
   that no machines are damaged.

2.1.1.  Approaches

   Control models, in general, can become involved but there is a
   variety of control algorithms that are composed of simple
   computations such as matrix multiplication.  As these are supported
   by programmable network devices, it is possibile to compose
   simplified approximations of the more complex algorithms and deploy
   them in the network.  While the simplified versions induce a more
   inaccurate control, they allow for a quicker response and might be
   sufficient to operate a basic tight control loop while the overall
   control can still be exercised from the cloud.  The problem, however,
   is that networking devices typically only allow for integer precision
   computation while floating-point precision is needed by most control
   algorithms.  Early approaches like [RUETH] have already shown the
   general applicability of such ideas, but there are still a lot of
   open research questions not limited to the following:

   o  How can one derive the simplified versions of the overall
      controller?




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      *  How complex can they become?

      *  How can one take the limited computational precision of
         networking devices into account when making them?

   o  How does one distribute the simplified versions in the network?

   o  How does the overall controller interact with the simplified
      versions?

3.  Large Volume Applications/ Traffic Filtering

   In the IIoT, processes and machines can be monitored more effectively
   resulting in more available information.  This data can be used to
   deploy machine learning (ML) techniques and consequently help to find
   previously unknown correlations between different components of the
   production which in turn helps to improve the overall production
   system.  Newly gained knowledge can be shared between different sites
   of the same company or even between different companies.

   Traditional company infrastructure is neither equipped for the
   management and storage of such large amounts of data nor for the
   computationally expensive training of ML approaches.  Similar to the
   considerations in Section 2, off-premise cloud platforms offer cost-
   effective solutions with a high degree of flexibility and
   scalability.  While the unpredictable latency of the Internet is only
   a subordinate problem for this use case, moving all data to off-
   premise locations primarily poses infrastructural challenges which
   are presented in more detail in the following.

3.1.  Characterization and Requirements

   Processes in the industrial domain are monitored by distributed
   sensors which range from simple binary (e.g., light barriers) to
   sophisticated sensors measuring the system with varying degrees of
   resolution.  Sensors can further serve different purposes, as some
   might be used for time-critical process control while others are only
   used as redundant fallback platforms.  Overall, there is a high level
   of heterogeneity which makes managing the sensor output a challenging
   task.

   Depending on the deployed sensors and the complexity of the observed
   system, the resulting overall data volume can easily be in the range
   of several Gbit/s [GLEBKE].  Using off-premise clouds for managing
   the data requires uploading or streaming the growing volume of sensor
   data using the companies' Internet access which is typically limited
   to a few hundred of Mbit/s.  While large networking companies can
   simply upgrade their infrastructure, most industrial companies rely



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   on traditional ISPs for their Internet access.  Higher access speeds
   are hence tied to higher costs and, above all, subject to the supply
   of the ISPs and consequently not always available.  A major challenge
   is thus to devise a methodology that is able to handle such amounts
   of data over limited access links.

   Another aspect is that business data leaving the premise and control
   of the company further comes with security concerns, as sensitive
   information or valuable business secrets might be contained in it.
   Typical security measures such as encrypting the data make in-network
   computing techniques hardly applicable as they typically work on
   unencrypted data.  Adding security to in-network computing
   approaches, either by adding functionality for handling encrypted
   data or devising general security measures, is thus an auspicious
   field for research which we describe in more detail in Section 5.

3.2.  Approaches

   There are at least two concepts which might be suitable for reducing
   the amount of transmitted data in a meaningful way:

   1.  filtering out redundant or unnecessary data

   2.  aggregating data by applying pre-processing steps within the
       network

   Both concepts require detailed knowledge about the monitoring
   infrastructure at the factories and the purpose of the transmitted
   data.

3.2.1.  Traffic Filters

   Sensors are often set up redundantly, i.e., part of the collected
   data might also be redundant.  Moreover, they are often hard to
   configure or not configurable at all which is why their resolution or
   sampling frequency is often larger than required.  Consequently, it
   is likely that more data is transmitted than is needed or desired.  A
   trivial idea for reducing the amount of data is thus to filter out
   redundant or undesired data before it leaves the premise using simple
   traffic filters that are deployed in the on-premise network.  There
   are different approaches to how this topic can be tackled.  A first
   step would be to scale down the available sensor data to the data
   rate that is needed.  For example, if a sensor transmits with a
   frequency of 5 kHz, but the control entity only needs 1 kHz, only
   every fifth packet containing sensor data is let through.
   Alternatively, sensor data could be filtered down to a lower
   frequency while the sensor value is in an uninteresting range, but
   let through with higher resolution once the sensor value range



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   becomes interesting.  It is important that end-hosts are informed
   about the filtering so that they can distinguish between data loss
   and data filtered out on purpose.

   In this context, the following research questions can be of interest:

   o  How can traffic filters be designed?

   o  How can traffic filters be coordinated and deployed?

   o  How can traffic filters be changed dynamically?

   o  How can traffic filtering be signaled to the end-hosts?

3.2.2.  In-Network (Pre-)Processing

   There are manifold computations that can be performed on the sensor
   data in the cloud.  Some of them are very complex or need the
   complete sensor data during the computation, but there are also
   simpler operations which can be done on subsets of the overall
   dataset or earlier on the communication path as soon as all data is
   available.  One example is finding the maximum of all sensor values
   which can either be done iteratively on each intermediate hop or at
   the first hop, where all data is available.

   Using expert knowledge about the exact computation steps and the
   concrete transmission path of the sensor data, simple computation
   steps can be deployed in the on-premise network to reduce the overall
   data volume and potentially speed up the processing time in the
   cloud.

   Related work has already shown that in-network aggregation can help
   to improve the performance of distributed ML applications [SAPIO].
   Investigating the applicability of stream data processing techniques
   to programmable networking devices is also interesting, because
   sensor data is usually streamed.  In this context, the following
   research questions can be of interest:

   o  Which (pre-)processing steps can be deployed in the network?

      *  How complex can they become?

   o  How can applications incorporate the (pre-)processing steps?

   o  How can the programming of the techniques be streamlined?






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4.  Industrial Safety (Dead Man's Switch)

   Despite increasing automation in production processes, human workers
   are still often necessary.  Consequently, safety measures have a high
   priority to ensure that no human life is endangered.  In traditional
   factories, the regions of contact between humans and machines are
   well-defined and interactions are simple.  Simple safety measures
   like emergency switches at the working positions are enough to
   provide a decent level of safety.

   Modern factories are characterized by increasingly dynamic and
   complex environments with new interaction scenarios between humans
   and robots.  Robots can either directly assist humans or perform
   tasks autonomously.  The intersect between the human working area and
   the robots grows and it is harder for human workers to fully observe
   the complete environment.

   Additional safety measures are essential to prevent accidents and
   support humans in observing the environment.  The increased
   availability of sensor data and the detailed monitoring of the
   factories can help to build additional safety measures if the
   corresponding data is collected early at the correct position.

4.1.  Characterization and Requirements

   Industrial safety measures are typically hardware solutions because
   they have to pass rigorous testing before they are certified and
   deployment-ready.  Standard measures include safety switches, which
   need to be triggered manually, and light barriers.  Additionally, the
   working area can be explicitly divided into 'contact' and 'safe'
   areas, indicating when workers have to watch out for interactions
   with machinery.

   These measures are static solutions, potentially relying on
   specialized hardware, and are challenged by the increased dynamics of
   modern factories where the factory configuration can be changed on
   demand.  Software solutions offer higher flexibility as they can
   dynamically respect new information gathered by the sensor systems.
   Depending on the corresponding occupational safety laws, the software
   has to satisfy stringent requirements which cannot be satisfied by
   regular best-effort networks.

4.1.1.  Approaches

   Software-based solutions can take advantage of the large amount of
   available sensor data.  Different safety indicators within the
   production hall can be combined within the network so that
   programmable networking devices can give early responses if a



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   potential safety breach is detected.  A rather simple possibility
   could be to track the positions of human workers and robots.
   Whenever a robot gets too close to a human in a non-working area or
   if a human enters a defined safety zone, robots are stopped to
   prevent injuries.  More advanced concepts could also include image
   data or combine arbitrary sensor data.

   In this context, the following research questions can be of interest:

   o  How can the software give guaranteed safety over best-effort
      networks?

   o  Which sensor information can be combined and how?

5.  Security Considerations

   Current in-network computing approaches typically work on unencrypted
   plain text data because today's networking devices usually do not
   have crypto capabilities.  As is already mentioned in Section 3.1,
   this above all poses problems when business data, potentially
   containing business secrets, is streamed into remote computing
   facilities and consequently leaves the control of the company.
   Insecure on-premise communication within the company and on the shop-
   floor is also a problem as machines could be intruded from the
   outside.  It is thus crucial to deploy security and authentication
   functionality on on-premise and outgoing communication although this
   might interfere with in-network computing approaches.  Ways to
   implement and combine security measures with in-network computing are
   described in more detail in [I-D.fink-coin-sec-priv].

6.  IANA Considerations

   N/A

7.  Conclusion

   In-network computing concepts have the potential to improve
   industrial applications.  There are at least three scenarios for
   which in-network processing can be beneficial, each having a unique
   set of requirements.

   In the control scenario, tight latency constraints in the single
   digit millisecond range have to be satisfied despite the use of cloud
   platforms and the corresponding unstable latency of the Internet.

   In a second scenario, large amounts of data have to be transmitted to
   cloud platforms for further evaluation.  One important task here is
   to reduce the amount of data that needs to be transmitted as the



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   available Internet access speed is most likely non-sufficient.  Apart
   from that, security measures have to be implemented as business data
   is transmitted to the Internet.

   Regarding safety, software-based measures often lack the required
   guarantees and do not withstand the testing for certification.  In-
   network processing with its potential for early responses can be a
   solution by combining different sensor outputs early and acting
   quickly.

8.  Informative References

   [GLEBKE]   Glebke, R., "A Case for Integrated Data Processing in
              Large-Scale Cyber-Physical Systems", DOI: 10125/60162, in
              HICSS, January 2019.

   [I-D.fink-coin-sec-priv]
              Fink, I. and K. Wehrle, "Enhancing Security and Privacy
              with In-Network Computing", draft-fink-coin-sec-priv-00
              (work in progress), March 2020.

   [I-D.mcbride-edge-data-discovery-overview]
              McBride, M., Kutscher, D., Schooler, E., and C. Bernardos,
              "Edge Data Discovery for COIN", draft-mcbride-edge-data-
              discovery-overview-03 (work in progress), January 2020.

   [RUETH]    Rueth, J., "Towards In-Network Industrial Feedback
              Control", DOI: 10.1145/3229591.3229592, in ACM SIGCOMM
              NetCompute, August 2018.

   [SAPIO]    Sapio, A., "Scaling Distributed Machine Learning with In-
              Network Aggregation", 2019,
              <https://arxiv.org/abs/1903.06701>.

   [TSN]      "Time-Sensitive Networking (TSN) Task Group", 2019,
              <https://1.ieee802.org/tsn/>.

Authors' Addresses

   Ike Kunze
   RWTH Aachen University
   Ahornstr. 55
   Aachen  D-50274
   Germany

   Phone: +49-241-80-21422
   Email: kunze@comsys.rwth-aachen.de




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   Klaus Wehrle
   RWTH Aachen University
   Ahornstr. 55
   Aachen  D-50274
   Germany

   Phone: +49-241-80-21401
   Email: wehrle@comsys.rwth-aachen.de











































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