Figure - uploaded by Jean Ibarz
Content may be subject to copyright.
Source publication
The main promise of intelligent transportation systems (ITS) is that leveraging the information sensed by millions of vehicles will increase the quality of the user's experience. However, the unpredictable nature of road events, combined with a projected network overload, calls for a careful optimization of the vehicles' data transfers, taking into...
Contexts in source publication
Context 1
... the number of units of execution to process a job is directly related to the number of packets to transmit a message. The analogy between our scheduling problem and the real-time scheduling problem is synthesized in Table 1. Following the usual terminology [13], a real-time job is: ...Citations
... The algorithm aims to reduce necessary transmissions and improve the data quality. Jean et al. [22] proposed a method to optimize vehicle-to-cloud data transfers. The algorithm increases its coverage of use-case representation by generating the workloads. ...
Edge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three-tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.
... Soft real-time applications are generalizations of hard real-time ones having weaker timeliness criteria. The results generated after a task's deadline may still have utility for the system, but with degraded performance, [40]. ...
Cloud computing platforms support the Internet of Vehicles, but the main bottlenecks are high latency and massive data transmission in cloud-based processing. Vehicular fog computing has emerged as a promising paradigm to accommodate the increasing computational needs of vehicles. It provides low latency network services that are most important for latency-sensitive tasks. The dynamic nature of VFC, having vehicles with heterogeneous computing resources, vehicle mobility, and diverse tasks with different priorities are the main challenges in vehicular fog networks. In VFC, vehicles can share their idle compute resources with other task-generating vehicles. So, scheduling the tasks on the idle resources of resource-limited vehicles is very important. Existing solutions use a heuristic approach to solve this issue but lack generalizability and adaptability. In this paper, we describe a PPO-based intelligent, priority and deadline-aware online and distributed resource allocation and task scheduling algorithm, called IRATS, in vehicular fog networks. IRATS formulates the resource allocation problem as a Markov decision process to minimize the waiting time and delay of tasks. For vehicles sharing their idle resources, we design a task scheduler for the orderly execution of received tasks according to their priorities using multi-level queues. We conducted extensive simulations using SUMO, OMNeT++, Veins, and veins-gym to validate the effectiveness of the presented algorithm. The simulation results confirm that the proposed algorithm improves the percentage of in-time completed tasks and decreases the packet loss, waiting time, and end-to-end delay as compared to random, A2C, and DQN algorithms considering the task priority and link duration of vehicles.
... Nous obtenons des résultats différents de ceux présentés dans la littérature, et proposons une explication à cela. Ces travaux ont fait l'objet d'une publication [Ibarz et al., 2020] pour la conférence RTNS 2020 1 . ...
... 1. High latency 2. Require routing protocols Scheduling [33], [34] Cloud-assisted service message [35] Security and privacy [36] Testbed & simulation framework [37] V2D ...
As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges.
Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.
As vehicles playing an increasingly important role in people’s daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.
