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COIN                                                            I. Kunze
Internet-Draft                                                  J. Rueth
Intended status: Informational                                 K. Wehrle
Expires: January 5, 2020                          RWTH Aachen University
                                                            July 4, 2019


             Industrial Use Cases for In-Network Computing
                draft-kunze-coin-industrial-use-cases-00

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, however, 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 can be a solution to this challenge
   which makes in-network computing concepts a promising starting point.
   This document discusses select 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

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on January 5, 2020.







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Copyright Notice

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   This document is subject to BCP 78 and the IETF Trust's Legal
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  In-Network Control / Time-sensitive applications  . . . . . .   4
     2.1.  Characterization and Requirements . . . . . . . . . . . .   5
       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



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   well-suited for traditional industrial settings where the
   communication paths are encapsulated at the respective factory sites
   and where the communication patterns are well understood.  Following
   the vision of the Industrial Internet of Things (IIoT), more and more
   parts of the industrial production domain are interconnected.  This
   increases the complexity of the industrial networks, making them more
   dynamic and creating 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.draft-mcbride-edge-data-discovery-overview-01], EDGE DEVICES,
   such as sensors and actuators, constitute the boundary between
   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 or manipulate the physical
   world by executing actions after receiving (simple) control
   information.  The processing of the sensor data as well as the
   creation of the control information is done on COMPUTING DEVICES.
   They range from small-powered controllers in close proximity 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 which only supports hosts of specific
   vendors.

   The challenge is to develop concepts which can include off-premise
   entities (such as distant cloud platforms) as well as proprietary
   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.  In an effort to showcase potential requirements for the
   domain of industrial production, we characterize and analyze three
   distinct scenarios to illustrate how in-network computations can be
   helpful.







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    --------
    |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.

2.  In-Network Control / Time-sensitive applications

   The control of physical processes and components of a production line
   is a cornerstone of the industrial domain.  It 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 in close proximity 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




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   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 is 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.

   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 about 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 important,
   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.  This is
   especially true if off-premise cloud platforms are included due to
   the unpredictable latency of the Internet.

   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.

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

2.1.1.  Approaches

   Control models in general can become complex 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 a possibility to compose simplified
   approximations of the more complex algorithms and deploy them in the
   network.  While the simplified versions induce a more inaccurate



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   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?

      *  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 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 and security
   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
   complex sensors measuring the system with varying degrees of
   resolution.  Sensors can further serve different purposes, as some



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   might be used for the time-critical process control while others are
   only used as redundant fall back 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
   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 methodology which 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 makes 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 a very promising
   field for research.

3.2.  Approaches

   While there is no work on the question of security yet, 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 preprocessing 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



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   sampling frequency is often larger than required.  Consequently, it
   is likely that more data is transmitted than is actually 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.  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?

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 sensors 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 machine learning
   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.  This gives safety measures 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 important 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.  Common 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 special
   hardware, and are challenged by the increased dynamics of modern
   factories.  Software solutions offer a 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 very strict 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
   potential safety breach is detected.  A rather simple possibility



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   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 certain 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

   N/A

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
   available Internet access speed is most likely non-sufficent.  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.







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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.draft-mcbride-edge-data-discovery-overview-01]
              McBride, M., Kutscher, D., Schooler, E., and C. Bernardos,
              "Overview of Edge Data Discovery", draft-mcbride-edge-
              data-discovery-overview-01 (work in progress), March 2019.

   [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


   Jan Rueth
   RWTH Aachen University
   Ahornstr. 55
   Aachen  D-50274
   Germany

   Phone: +49-241-80-21417
   Email: rueth@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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