Elastic Queueing Engine for Time Sensitive Networking

Mariño, Angela Gonzalez; Fons, Francesc; Li, Ming; Aróstegui, Juan Manuel Moreno

doi:10.1109/vtc2021-spring51267.2021.9448758

Cited by 8 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Queuing: This provides the required buffering to accommodate traffic going from input to output ports without losses and effectively handle traffic congestions. The architecture and strategy followed in the queueing engine, are described in [83].…”

Section: Viu Components Descriptionmentioning

confidence: 99%

Elastic Gateway Functional Safety Architecture and Deployment: A Case Study

et al. 2022

Self Cite

View full text Add to dashboard Cite

The automotive industry has started its transformation towards Software-Defined Vehicles. This transformation is driven by the rise of the number of vehicle features, the high complexity of these features and their constraining availability requirements which affect all the players (Original Equipment Manufacturers, Tier1 and Tier2) of the sector. In the context of this transformation, our target, from functional safety point of view, is to, inter alia, provide an easy-to-use and safety-compliant execution and development flow and simplify the development and argumentation for safety by providing a) a pre-certified execution environment with safety design patterns and best-in-class safety measures and b) processes and tooling to minimize the system integrator's effort. Therefore, in this work we propose a top-down approach where we first define a New Generation In-Vehicle Network, NGIVN, capable of fulfilling the performance (e.g. high bandwidth, low end-2-end delay), safety-related availability (e.g. Autonomous Driving / Advanced Driver-Assistance Systems (AD/ADAS) up to SAE level 5) and safety requirements of modern vehicles. Also, we illustrate the advantages of this approach by deriving the functional and safety attributes of an Automotive Gateway SoC, named Elastic Gateway and destined to be part of the NGIVN. Through the deployment of the Elastic Gateway functional safety concept we demonstrate the flexibility provided by our approach with regards to the design of elements of the NGIVN.

show abstract

Section: Viu Components Descriptionmentioning

confidence: 99%

Elastic Gateway Functional Safety Architecture and Deployment: A Case Study

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that reordering the frames of a stream is bad for realtime applications, and most network equipment take extra care to avoid reordering frames. In [38] the elastic queueing engine (EQE) was proposed, which uses dynamic queue lengths, dynamic internal priority values, and adaptive GCL to avoid dropping frames due to queue size limitations. This is accompanied by a hardware coprocessor design for real-time operation in in-vehicle networks.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Time-Aware Shaper for Time-Sensitive Networking

2022

View full text Add to dashboard Cite

The Time Sensitive Networking Task Group of the IEEE 802.1 Working Group has developed a number of enhancements to the priority queueing architecture used by Ethernet. Perhaps the most notable one is the time-aware shaper (TAS), which can perfectly isolate priority classes in time with periodically scheduled traffic gates, thereby enabling ultra low latency applications to coexist with lower priority traffic on the same network. It requires near-perfect clock synchronization across the entire network, though, and a central network controller to calculate the timings for the traffic gates, based on known propagation delays on the network links. As a cheaper alternative, we have developed the asynchronous TAS that needs no clock synchronization, network planning, or a central controller. It uses local processes at each switch port to track the high priority streams, predict the arrival times of their next frames, and control the traffic gates such that the low priority streams are not interfering. We show how a simple predictor with minimal computation requirements can be used to track the high priority streams, and we demonstrate via simulations that this gating mechanism can protect the high priority traffic from interference.

show abstract

“…It predicts the average traffic length and average waiting time at certain intersections. This work and [ 17 , 18 ] only involve the delay performance analysis but lack the optimal planning to improve the performance. The paper [ 19 ] proposes a computational model of drivers’ lateral control based on the queuing network cognitive architecture and the driver preview model about drivers’ lateral control activities.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Intelligent Traffic Planning in the Internet of Vehicles

Zhu

2024

Sensors

View full text Add to dashboard Cite

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles.

show abstract

Elastic Queueing Engine for Time Sensitive Networking

Cited by 8 publications

References 5 publications

Elastic Gateway Functional Safety Architecture and Deployment: A Case Study

Elastic Gateway Functional Safety Architecture and Deployment: A Case Study

Asynchronous Time-Aware Shaper for Time-Sensitive Networking

Collaborative Intelligent Traffic Planning in the Internet of Vehicles

Contact Info

Product

Resources

About