Internet-Draft | Computing-Aware ITS Applicability | November 2024 |
Jeong & Mugabarigira | Expires 8 May 2025 | [Page] |
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.¶
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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 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].¶
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].¶
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.¶
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].¶
This section explains four use cases about the applicability of CATS to Computing-Aware ITS.¶
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.¶
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.¶
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.¶
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.¶
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.¶
This document does not require any IANA actions.¶
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].¶
The following changes are made from draft-jeong-cats-its-use-cases-03:¶
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).¶
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:¶