Computing-Aware Traffic Steering Working Group             J. Jeong, Ed.
Internet-Draft                                           B. Mugabarigira
Intended status: Informational                   Sungkyunkwan University
Expires: 8 May 2025                                      4 November 2024


    Applicability of Computing-Aware Traffic Steering to Intelligent
                         Transportation Systems
                   draft-jeong-cats-its-use-cases-04

Abstract

   This document describes the applicability of Computing-Aware Traffic
   Steering (CATS) to Intelligent Transportation Systems (ITS).  CATS
   provides the steering of packets of a traffic flow for a specific
   service request toward the corresponding service instance at an edge
   computing server at a service site.  CATS are applicable for
   Computing-Aware ITS including (i) Context-Aware Navigation Protocol
   (CNP) for Terrestrial Vehicles and Unmanned Aerial Vehicles (UAV),
   (ii) Edge-Assisted Cluster-Based MAC Protocol (ECMAC) for Software-
   Defined Vehicles, and (iii) Self-Adaptive Interactive Navigation Tool
   (SAINT) for Cloud-Based Navigation Services, and (iv) Cloud-Based
   Drone Navigation (CBDN) for Efficient Drone Battery Charging.

Status of This Memo

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   This Internet-Draft will expire on 8 May 2025.

Copyright Notice

   Copyright (c) 2024 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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   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Vehicular Network Architecture  . . . . . . . . . . . . . . .   3
   4.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Context-Aware Navigation Protocol . . . . . . . . . . . .   5
     4.2.  Edge-Assisted Cluster-Based MAC Protocol  . . . . . . . .   6
     4.3.  Self-Adaptive Interactive Navigation Tool for Cloud-Based
           Navigation  . . . . . . . . . . . . . . . . . . . . . . .   9
     4.4.  Cloud-Based Drone Navigation (CBDN) for Efficient Battery
           Charging in Drone Networks  . . . . . . . . . . . . . . .  11
   5.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .  13
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  14
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  14
   Appendix A.  Changes from draft-jeong-cats-its-use-cases-03 . . .  16
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  16
   Contributors  . . . . . . . . . . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  17

1.  Introduction

   Nowadays, various networked services are provided by leveraging edge
   computing infrastructure.  Either a closest or a lightest edge
   computing server (simply called an edge server) can be selected to
   serve a request service.  In this trend, Computing-Aware Traffic
   Steering (CATS) is standardized to provide the steering of packets of
   a traffic flow for a specific service request toward the
   corresponding service instance at an edge server at a service site
   [I-D.ietf-cats-usecases-requirements][I-D.ietf-cats-framework].

   This document proposes four use cases about the applicability of CATS
   for Computing-Aware Intelligent Transportation Systems (ITS).  They
   are (i) Context-Aware Navigation Protocol for Terrestrial Vehicles
   and Unmanned Aerial Vehicles (UAV) [CNP-Vehicle] [CNP-UAV], (ii)
   Edge-Assisted Cluster-Based MAC Protocol for Software-Defined



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   Vehicles (SDV) [ECMAC], (iii) Self-Adaptive Interactive Navigation
   Tool (SAINT) for Cloud-Based Navigation Services [SAINT], and (iv)
   Cloud-Based Drone Navigation (CBDN) for Efficient Drone Battery
   Charging [CBDN].

2.  Terminology

   This document uses the terminology described in
   [I-D.ietf-cats-usecases-requirements] and [I-D.ietf-cats-framework].
   In addition, the following terms are defined below:

   *  Context-Aware Navigation Protocol (CNP): It is an application
      protocol that enables either terrestrial vehicles (i.e., ground
      vehicles) or Unmanned Aerial Vehicles (UAV) to move in road
      networks or fly in the sky to maneuver safely without collisions,
      respectively [CNP-Vehicle][CNP-UAV].

   *  Edge-Assisted Cluster-Based MAC Protocol (ECMAC): It is a Media
      Access Control (MAC) protocol that enables Software-Defined
      Vehicles (SDV) to communicate with each other using Software-
      Defined Vehicular Networks with edge computing servers [ECMAC].

   *  Self-Adaptive Interactive Navigation Tool (SAINT): It is an
      application protocol that guides terrestrial vehicles to navigate
      efficiently towards their destination through the interaction
      between the vehicles and the vehicular cloud for navigation
      services [SAINT].

   *  Cloud-Based Drone Navigation (CBDN): It is an application protocol
      for efficient drone battery charging in drone networks by finding
      globally coordinated drone routes that minimize the total traffic
      delay in a drone network while reducing the overall Quick Battery-
      Charging Machine (QCM) congestion level [CBDN].

3.  Vehicular Network Architecture

   This section explains a vehicular network architecture for vehicles
   in Computing-Aware ITS.

   Software-Defined Vehicles (SDV) include terrestrial vehicles and
   Unmanned Aerial Vehicles (UAV).  The standardization and
   implementation of SDVs are performed by AUTOSAR [AUTOSAR], Eclipes
   SDV [Eclipse-SDV], and COVESA [COVESA].  These SDVs need to
   communicate with each other to avoid collisions or accidents.







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   Figure 1 shows a Vehicular Network Architecture for Software-Defined
   Vehicles (SDV) such as terrestrial vehicles and Unmanned Aerial
   Vehicles (UAV).  This vehicular network architecture is based on the
   vehicular network architecture for IPv6 Wireless Access in Vehicular
   Environments (IPWAVE) in [RFC9365].

                              Vehicular Cloud
               *******************************************
             *                                             *
            *              +------------------+             *
           *               | Cloud Controller |              *
           *               +------------------+              *
           *                         ^                       *
            *                        |                      *
             *                       v                     *
               *******************************************
                 ^   Edge-Cloud1    ^   Edge-Cloud2    ^   Edge-Cloud3
                 | +------------+   | +------------+   | +------------+
                 | |Edge-Server1|   | |Edge-Server2|   | |Edge-Server3|
                 | +------------+   | +------------+   | +------------+
                 |   ^              |   ^              |   ^
                 |   |              |   |              |   |
                 v   V              v   V              v   V
               +---------+         +---------+        +---------+
               | IP-RSU1 |<------->| IP-RSU2 |<------>| IP-RSU3 |
               +---------+         +---------+        +---------+
                    ^                   ^                    ^
                    :                   :                    :
           +-----------------+ +-----------------+   +-----------------+
           |        : V2I    | |        : V2I    |   |       : V2I     |
           |        v        | |        v        |   |       v         |
+--------+ |   +--------+    | |   +--------+    |   |   +--------+    |
|  SDV1  |===> |  SDV2  |===>| |   |  SDV3  |===>|   |   |  SDV4  |===>|
+--------+<...>+--------+<........>+--------+    |   |   +--------+    |
           V2V     ^         V2V        ^        |   |        ^        |
           |       : V2V     | |        : V2V    |   |        : V2V    |
           |       v         | |        v        |   |        v        |
           |  +--------+     | |   +--------+    |   |    +--------+   |
           |  |  SDV5  |===> | |   |  SDV6  |===>|   |    |  SDV7  |==>|
           |  +--------+     | |   +--------+    |   |    +--------+   |
           +-----------------+ +-----------------+   +-----------------+
                 Subnet1              Subnet2              Subnet3
                (Prefix1)            (Prefix2)            (Prefix3)

        <----> Wired Link   <....> Wireless Link   ===> Moving Direction

    Figure 1: Vehicular Network Architecture for Software-Defined
                               Vehicles



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4.  Use Cases

   This section explains four use cases about the applicability of CATS
   to Computing-Aware ITS.

4.1.  Context-Aware Navigation Protocol

   A connected network of automated vehicles on roads can increase the
   driving safety of driverless vehicles (i.e., autonomous vehicles).
   The critical level of dangerous situations on the road while driving
   can be increased by the speed, orientation, and traffic density of
   the vehicles involved.  Therefore, there is a need for a maneuvering
   mechanism that handles both the current driving vehicle and the
   oncoming vehicles headed toward an emergency zone (e.g., road hazard
   and road accident spot).

                    Edge Cloud
                  +-------------+
                  | Edge-Server1|
                  +-------------+
                    ^         ^
                    |         |
                    V         V
            +---------+      +---------+           +---------+
            | IP-RSU1 |<---->| IP-RSU2 |<---...--->| IP-RSUn |
            +---------+      +---------+           +---------+
                 ^             ^                        ^
                 :             :                        :
    +-------------------+ +----------+     +-----------------------------+
    |            : V2I  | |    : V2I |     |            : V2I            |
    |            v      | |    v     |     |+----+V2V   v    V2V+----+   |
    |      V2V +----+   | | +----+   |     || CM1|<-->+----+<-->| CM2|==>|
    |+----+<-->| CH1|==>| | | CH2|==>|     |+----+==> | CHn|==> +----+   |
    || CM1|==> +----+   | | +----+   |     |          +----+             |
    |+----+      ^      | |    ^     | ... |             ^               |
    |            | V2V  | |    | V2V |     |             | V2V           |
    |            v      | |    v     |     |             v               |
    |          +----+   | | +----+   |     |          +----+             |
    |          | CM2|==>| | | CM1|==>|     |          | CM3|==>          |
    |          +----+   | | +----+   |     |          +----+             |
    +-------------------+ +----------+     +-----------------------------+
           Cluster1         Cluster2                  Clustern

    <----> Wired Link   <....> Wireless Link   ===> Moving Direction

    Figure 2: The Illustration of Context-Aware Navigator Protocol





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   Context-Aware Navigation Protocol (CNP) enhances the safety of
   vehicles driving in urban roads [CNP-Vehicle][CNP-UAV].  Firstly, CNP
   includes a collision avoidance module that builds on both vehicular
   networks and on-board sensors to track vehicles' behaviors, and this
   module analyzes the driving risks to determine the necessary
   maneuvers in dangerous situations.  Secondly, CNP establishes a
   collision mitigation strategy that limits the severity of collision
   damages in hazardous road during non-maneuverable scenarios.  Through
   a theoretical analysis as well as extensive simulations, the
   effectiveness of CNP is shown in terms of the reduction of both
   communication overhead and the risk of road collisions.

   To use CNP, vehicles need to report their mobility information (e.g.,
   vehicle identifier, destination, current position, direction, and
   speed) to a central cloud or an edge cloud for a CNP-based vehicle
   collision avoidance service as shown in Figure 2.  Service instances
   at either the edge cloud or the central cloud need to work for the
   vehicles.  The packets with the mobility information per vehicle need
   to steered to an appropriate service instance for CNP.  The service
   instance needs to provide a appropriate maneuver direction to each
   vehicle moving on the roadway.

   Since the vehicle is moving along the roadway, to serve the vehicle
   for collision avoidance, a new service instance needs to be selected
   for it, considering the network delay between the vehicle and service
   instance and also computing resources for the service instance.  For
   the service instances to continue to compute the maneuvers smoothly,
   they need to exchange the mobility information as context while the
   vehicles are moving and change their service instance over time.
   That is, the context migration should be supported in the CATS
   infrastructure having the central clouds and the edge clouds to
   foster service instances.

4.2.  Edge-Assisted Cluster-Based MAC Protocol

   Vehicular networks have emerged as a promising means to mitigate
   safety hazards in modern transportation systems.  On highways,
   emergency situations associated with vehicles necessitate a reliable
   Media Access Control (MAC) protocol that can provide timely warnings
   of possible vehicle collisions.











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   An Edge-Assisted Cluster-Based MAC Protocol (ECMAC) is a vehicular
   MAC protocol for reliable and fast packet dissemination in software-
   defined vehicular networks [ECMAC].  To reduce the control messaging
   overhead for clustering, ECMAC separates the cluster control plane
   (i.e., managing cluster formation) from the data plane (i.e., actual
   data transmission and forwarding) by using a software-defined network
   controller in a cellular network edge server as illustrated in
   Figure 3.

   For transmitting packets effectively and efficiently, ECMAC tries to
   channel interference minimization among adjacent clusters by using a
   joint optimization of channel assignment and a time slot scheduling.
   The joint optimization consists of two phases such as the channel
   assignment phase and the time slot allocation phase.  In the first
   phase for the channel assignment, ECMAC allocates different wireless
   channels to the adjacent channels by minimizing the total inter-
   cluster interference by reusing the available channels.  In the
   second phase for the time slot allocation, ECMAC uses a time-division
   multiple access (TDMA) schedule algorithm to guarantee a high
   reliability and a low latency.  The TDMA schedule in ECMAC is
   determined by a joint optimization process in the cellular edge,
   which is formulated as a binary integer linear programming problem
   and solved by a heuristic approach based on the divide-and-conquer
   paradigm.  This joint optimization process minimizes the signal
   interference by jointly considering channel assignment and time slot
   allocation, thereby ensuring reliable communication.  Through
   extensive simulations, the effectiveness of ECMAC is demonstrated a
   higher delivery ratio of emergency packets than the legacy data
   delivery approaches.






















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                      Vehicular Cloud
              *******************************
            *            Edge Cloud           *
           *    +--------------------------+    *
          *     |       Edge-Server1       |     *
          *     |  +--------------------+  |     *
          *     |  |Cluster Formation,  |  |     *
          *     |  |Channel Assignment, |  |     *
          *     |  |Time Slot Allocation|  |     *
           *    |  +--------------------+  |    *
            *   +--------------------------+   *
              ********************************
                     ^                       ^
                     |                       |
                     V                       V
               +---------+               +---------+
               | IP-RSU1 |<-----...----->| IP-RSUm |
               +---------+               +---------+
                    ^                         ^
                    :                         :
       +-------------------+     +-----------------------------+
       |            : V2I  |     |            : V2I            |
       |            v      |     |+----+ V2V  v   V2V +----+   |
       |      V2V +----+   |     || CM1|<-->+----+<-->| CM2|==>|
       |+----+<-->| CH1|==>|     |+----+==> | CHn|==> +----+   |
       || CM1|==> +----+   |     |          +----+             |
       |+----+      ^      | ... |             ^               |
       |            | V2V  |     |             | V2V           |
       |            v      |     |             v               |
       |          +----+   |     |          +----+             |
       |          | CM2|==>|     |          | CM3|==>          |
       |          +----+   |     |          +----+             |
       +-------------------+     +-----------------------------+
              Cluster1                      Clustern

       <----> Wired Link   <....> Wireless Link   ===> Moving Direction

      Figure 3: The Illustration of Edge-Assisted Clusterer-Based MAC
                                  Protocol

   In ECMAC, the cellular network edge server can be implemented as a
   service instance in the CATS infrastructure.  In the same way with
   CNP, service instances need to efficiently perform the context
   migration (e.g., mobility information and cluster membership) of
   vehicles so that they can continue to form clusters of vehicles,
   allocate wireless channels to the vehicles, and assign time slots to
   the vehicles over time.




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4.3.  Self-Adaptive Interactive Navigation Tool for Cloud-Based
      Navigation

   Efficient navigation services are important in Intelligent
   Transportation Systems because they allow vehicles to move towards
   destinations quickly.  For this efficient navigation, vehicles need
   to interact with a central cloud or an edge cloud in real time.

   Self-Adaptive Interactive Navigation Tool (SAINT) is a cloud-based
   navigation guidance system for vehicular traffic optimization in road
   networks [SAINT].  The legacy navigation systems guide vehicles to
   take their navigation paths with real-time traffic statistics in road
   maps without considering the navigation paths of other vehicles.
   This uncoordinated navigation planning may incur traffic congestion
   in certain areas in the road networks.




































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                         Vehicular Cloud
              *************************************
            *              Edge Cloud               *
           *  +-----------------------------------+  *
          *   |   Edge Server    Edge Emergency   |   *
          *   |                       Center      |   *
          *   | +-------------+ +---------------+ |   *
          *   | |Road Traffic | |Road Emergency | |   *
          *   | |Information  | |Notification   | |   *
           *  | +-------------+ +---------------+ |  *
            * +-----------------------------------+ *
              *************************************
                ^                  ^              ^
                |                  |              |
                V                  V              V
           +---------+       +---------+        +---------+
           | IP-RSU1 |<----->| IP-RSU2 |<------>| IP-RSU3 |
           +---------+       +---------+        +---------+
                ^                   ^                   ^
                :                   :                   :
                :                   :                   :
          +-----------------+ +-----------------+ +-----------------+
          |     : V2I       | |     : V2I       | |     : V2I       |
          |     v           | |     v           | |     v           |
          |   +--------+    | |   +--------+    | |   +--------+    |
          |   |  SDV1  |===>| |   |  SDV2  |===>| |   |  SDV3  |===>|
          |   +--------+<........>+--------+    | |   +--------+    |
          |      ^         V2V        ^         | |        ^        |
          |      : V2V      | |       : V2V     | |        : V2V    |
          |      v          | |       v         | |        v        |
          |  +--------+     | |   +--------+    | |    +--------+   |
          |  |  SDV4  |===> | |   |  SDV5  |===>| |    |  SDV6  |==>|
          |  +--------+     | |   +--------+    | |    +--------+   |
          +-----------------+ +-----------------+ +-----------------+
                Subnet1             Subnet2             Subnet3
               (Prefix1)           (Prefix2)           (Prefix3)

       <----> Wired Link   <....> Wireless Link   ===> Moving Direction

     Figure 4: The Illustration of Self-Adaptive Interactive Navigation











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   On the other hand, SAINT uses a virtual metric called congestion
   contribution that estimates traffic congestion in each road segment
   in the current time and near-future time by considering the planned
   navigation paths of the vehices in the target road network.  SAINT
   guides each vehicle to have a certain-level detour in order to make
   the whole road network have spread vehicular traffic and lessen
   possible traffic congestion in certain road segments or
   intersections.

   For this cooperative navigation in SAINT, while vehicles are moving
   along the roadways, they need to send their periodic navigation
   queries and their mobility information to appropriate service
   instances in a central cloud or an edge cloud in the CATS
   infrastructure.  The service instances need to process their
   navigation queries and reply to them with good navigation paths,
   considersing the road-wide traffic optimization as depicted in
   Figure 4.  Due to the movement of the vehicles, the switching from a
   service instance to another service instance should be performed
   efficiently, considering the network delay between the service
   instance and each vehicle and the computing resources of the service
   instance.

   SAINT can support the efficient delivery of emergency vehicles such
   as ambulance and fire engine to a road accident spot by the
   management of a congestion contribution matrix in a target road
   network [SAINTplus].  It can not only guide vehicles within the
   accident spot, but also can detour vehicles approaching the accident
   spot.  This version of SAINT is called SAINT+.

4.4.  Cloud-Based Drone Navigation (CBDN) for Efficient Battery Charging
      in Drone Networks

   The growing popularity of Unmanned Aerial Vehicles (UAV) comes with a
   need to charge their battery at Quick Battery-Charging Machines
   (QCMs) due to their limited battery capacity.  Without drone
   coordination, a drone's choice for its QCM may lead to congestion
   resulting from multiple drones selecting the same QCM, thus
   increasing the drones' battery-charging delay due to the queueing day
   at the QCM.  This battery-charging delay leads to a long travel delay
   for each drone at the QCM.  A Cloud-Based Drone Navigation (CBDN)
   efficiently determines drone routes to minimize the overall QCM
   congestion level for all QCMs in a target drone network [CBDN].  It
   finds globally coordinated drone routes that minimize the total
   travel delay in a drone network by reducing the overall QCM
   congestion level.






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                        Vehicular Cloud
               *******************************
             *            Edge Cloud           *
            *    +--------------------------+    *
           *     |       Edge-Server1       |     *
           *     |  +--------------------+  |     *
           *     |  |Drone Registration, |  |     *
           *     |  |Drone Route Finding,|  |     *
           *     |  |QCM-Selection Scheme|  |     *
            *    |  +--------------------+  |    *
             *   +--------------------------+   *
               ********************************
                 ^            ^               ^
                 |            |               |
                 V            V               V
           +---------+     +---------+     +---------+
           | IP-RSU1 |<--->| IP-RSU2 |<--->| IP-RSU3 |
           +---------+     +---------+     +---------+
                 ^                ^                 ^
                 :                :                 :
       +-----------------+ +-----------------+ +-----------------+
       |         : V2I   | |      : V2I      | |    : V2I        |
       |         v       | |      v          | |    v            |
       |   +--------+    | |   +--------+    | |   +--------+    |
       |   |  UAV1  |===>| |   |  UAV2  |===>| |   |  UAV3  |===>|
       |   +--------+<........>+--------+    | |   +--------+    |
       |      ^         V2V        ^         | |        ^        |
       |      : V2V      | |       : V2I     | |        : V2I    |
       |      v          | |       v         | |        v        |
       |  +--------+     | |   +--------+    | |   +--------+    |
       |  |  UAV4  |===> | |   |  QCM1  |    | |   |  QCM2  |    |
       |  +--------+     | |   +--------+    | |   +--------+    |
       +-----------------+ +-----------------+ +-----------------+
             Subnet1             Subnet2             Subnet3
            (Prefix1)           (Prefix2)           (Prefix3)

       <----> Wired Link   <....> Wireless Link   ===> Moving Direction

         Figure 5: The Illustration of Cloud-Based Drone Navigation

   An edge cloud in the CATS infrastructure with computing and storage
   resources need to compute the trajectories of the drones (i.e., drone
   routes), along with their average speeds, source positions, and
   destination positions, as well as the battery charing loads at the
   QCMs.  The wireless communications between drones and infrastrucure
   nodes (e.g., edge server) can be either 5G and beyond 5G or wireless
   LAN, as illustated in Figure 5.  Drones interact with the edge server
   to compute navigation paths regarding the drone network-wide traffic



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   optimization of all drones in the drone network.  To decrease battery
   consumption, the drones only once report their mobility information
   (i.e., current position, destination, direction, and speed) to the
   edge computing decice to acquire their navigation paths.

   Upon the commencement of the drone service, each drone reports its
   mobility information to the edge server.  A drone's QCM reservation
   for battery charging acquires the most efficient shortest path
   regarding the drone-network-wide traffic optimization of all the
   drones in the drone network.  For this drone-network-wide traffic
   optimization, a drone sends its mobility information to the edge
   server before its departure, and the edge server computes an optimal
   navigation path to the drone and notifies the drone of the path in
   run time.

5.  Requirements

   This section specifies the requirements for the applicability of CATS
   to ITS use cases in Section 4.

   *  R1: Dynamic mapping between a required service and a service
      instance.  Both network delay and computing delay are considered
      over time.

   *  R2: Run-time context migration of vehicles between edge servers
      (i.e., service instances).  Each vehicle's context (e.g., mobility
      information, communications parameters (e.g., channel, time slot))
      is transferred to an appropriate service instance along with its
      movement over time.

   *  R3: Proactive load balancing among service instances considering
      the required Quality of Service (QoS) and Quality of Experience
      (QoE) for vehicles.  The trajectories of vehicles are considered
      for such load balancing.

   *  R4: Dynamic clustering of geographically adjacent vehicles.
      Clusters of vehicles are dynamically reconstructed over time.

   *  R5: Dynamic network configuration for vehicles and network
      forwarding entities (e.g., base stations and switches/routers).
      In wireless networks, network resources (e.g., channel and time
      slot) per vehicle are dynamically configured by base stations.  In
      wired networks, a network slice from a base station to a service
      instance are dynamically adjusted for each vehicle.







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   *  R6: Differentiated packet scheduling for service types.  Packets
      of real-time services (e.g., autonomous driving) and packets of
      non-real-time services (e.g., infotainment) are handled
      differently.

6.  IANA Considerations

   This document does not require any IANA actions.

7.  Security Considerations

   The same security considerations for Computing-Aware Traffic Steering
   (CATS) are applicable to the use cases for the Computing-Aware ITS
   [I-D.ietf-cats-usecases-requirements] [I-D.ietf-cats-framework].

8.  References

8.1.  Normative References

   [RFC9365]  Jeong, J., Ed., "IPv6 Wireless Access in Vehicular
              Environments (IPWAVE): Problem Statement and Use Cases",
              RFC 9365, DOI 10.17487/RFC9365, March 2023,
              <https://www.rfc-editor.org/info/rfc9365>.

8.2.  Informative References

   [I-D.ietf-cats-usecases-requirements]
              Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
              "Computing-Aware Traffic Steering (CATS) Problem
              Statement, Use Cases, and Requirements", Work in Progress,
              Internet-Draft, draft-ietf-cats-usecases-requirements-04,
              21 October 2024, <https://datatracker.ietf.org/doc/html/
              draft-ietf-cats-usecases-requirements-04>.

   [I-D.ietf-cats-framework]
              Li, C., Du, Z., Boucadair, M., Contreras, L. M., and J.
              Drake, "A Framework for Computing-Aware Traffic Steering
              (CATS)", Work in Progress, Internet-Draft, draft-ietf-
              cats-framework-04, 17 October 2024,
              <https://datatracker.ietf.org/doc/html/draft-ietf-cats-
              framework-04>.

   [AUTOSAR]  "AUTOSAR Adaptive Platform", Available: 
              https://www.autosar.org/standards/adaptive-platform, March
              2024.






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   [Eclipse-SDV]
              "Eclipse Software Defined Vehicle Working Group Charter",
              Available: https://www.eclipse.org/org/workinggroups/sdv-
              charter.php, March 2024.

   [COVESA]   "Connected Vehicle Systems Alliance",
              Available: https://covesa.global/, March 2024.

   [CNP-Vehicle]
              Mugabarigira, B., Shen, Y., Jeong, J., Oh, T., and H.
              Jeong, "Context-Aware Navigation Protocol for Safe Driving
              in Vehicular Cyber-Physical Systems", IEEE Transactions on
              Intelligent Transportation Systems, Volume 24, Issue 1,
              Available: https://ieeexplore.ieee.org/document/9921182,
              January 2023.

   [CNP-UAV]  Mugabarigira, B. and J. Jeong, "Context-Aware Navigation
              Protocol for Safe Flying of Unmanned Aerial Vehicles",
              KICS Winter Conference, Available: 
              http://iotlab.skku.edu/publications/international-
              journal/CNP-TITS-2023.pdf, January 2024.

   [ECMAC]    Shen, Y., Jeong, J., Jun, J., Oh, T., and Y. Baek, "ECMAC:
              Edge-Assisted Cluster-Based MAC Protocol in Software-
              Defined Vehicular Networks", IEEE Transactions on
              Vehicular Technology, Volume 73, Issue 9,
              Available: https://ieeexplore.ieee.org/document/10505005,
              September 2024.

   [SAINT]    Jeong, J., Jeong, H., Lee, E., Oh, T., and D. Du, "SAINT:
              Self-Adaptive Interactive Navigation Tool for Cloud-Based
              Vehicular Traffic Optimization", IEEE Transactions on
              Vehicular Technology, Volume 65, Issue 6,
              Available: https://ieeexplore.ieee.org/document/7243355,
              June 2016.

   [SAINTplus]
              Shen, Y., Lee, J., Jeong, H., Jeong, J., Lee, E., and D.
              Du, "SAINT+: Self-Adaptive Interactive Navigation Tool+
              for Emergency Service Delivery Optimization",
              IEEE Transactions on Intelligent Transportation Systems,
              Volume 19, Issue 4,
              Available: https://ieeexplore.ieee.org/document/7953571,
              April 2018.

   [CBDN]     Kim, J., Kim, S., Jeong, J., Kim, H., Park, J., and T.
              Kim, "CBDN: Cloud-Based Drone Navigation for Efficient
              Battery Charging in Drone Networks", IEEE Transactions on



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              Intelligent Transportation Systems, Volume 20, Issue 11,
              Available: https://ieeexplore.ieee.org/document/8574043,
              November 2019.

Appendix A.  Changes from draft-jeong-cats-its-use-cases-03

   The following changes are made from draft-jeong-cats-its-use-cases-
   03:

   *  This version specifies the requirements for the applicability of
      CATS to ITS use cases in Section 5.

Acknowledgments

   This work was supported by Institute of Information & Communications
   Technology Planning & Evaluation (IITP) grant funded by the Korea
   Ministry of Science and ICT (MSIT) (No.  RS-2024-00398199 and RS-
   2022-II221015).

   This work was supported in part by the National Research Foundation
   of Korea (NRF) grant funded by the Korea government, Ministry of
   Science and ICT (MSIT) (No. 2023R1A2C2002990).

Contributors

   This document is made by the group effort of CATS WG, greatly
   benefiting from inputs and texts by Peng Liu (China Mobile), Yong-
   Geun Hong (Daejeon University), and Joosang Youn (Dong-Eui
   University).  The authors sincerely appreciate their contributions.

   The following are coauthors of this document:

   Juwon Hong
   Department of Computer Science & Engineering
   Sungkyunkwan University
   2066 Seobu-Ro, Jangan-Gu
   Suwon
   Gyeonggi-Do
   16419
   Republic of Korea
   Phone: +82 31 299 4106
   Email: hongju2024@skku.edu
   URI:   http://iotlab.skku.edu/people-Joo-Won-Hong.php


   Yiwen Shen
   Department of Computer Science & Engineering
   Sungkyunkwan University



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   2066 Seobu-Ro, Jangan-Gu
   Suwon
   Gyeonggi-Do
   16419
   Republic of Korea
   Phone: +82 31 299 4106
   Email: chrisshen@skku.edu
   URI:   https://chrisshen.github.io/


Authors' Addresses

   Jaehoon Paul Jeong (editor)
   Department of Computer Science & Engineering
   Sungkyunkwan University
   2066 Seobu-Ro, Jangan-Gu
   Suwon
   Gyeonggi-Do
   16419
   Republic of Korea
   Phone: +82 31 299 4957
   Email: pauljeong@skku.edu
   URI:   http://iotlab.skku.edu/people-jaehoon-jeong.php


   Bien Aime Mugabarigira
   Department of Electrical & Computer Engineering
   Sungkyunkwan University
   2066 Seobu-Ro, Jangan-Gu
   Suwon
   Gyeonggi-Do
   16419
   Republic of Korea
   Phone: +82 10 5964 8794
   Email: bienaime@skku.edu
   URI:   http://iotlab.skku.edu/people-Bien-Aime.php















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