Connectivity-aware service provision in vehicular cloud

Sibai, Rayane El; Atéchian, Talar; Abdo, Jacques Bou; Tawil, Rami; Demerjian, Jacques

doi:10.1109/cloudtech.2015.7337017

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…The task allocation algorithm can be divided into the following four steps: (1) Initialize the Lagrange multipliers. (2) According to the given Lagrange multipliers, solve the optimization problem P21 through KKT conditions to obtain the optimal task allocation result; update the Lagrange multipliers with the method in formula (30)-(32). (3) Get the task allocation result by the convergence condition of Lagrange function.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…In a vehicle network [1], the cloud composed of vehicles with strong computing power is called VC [2]. The task offloading of the VC [3] has a significant impact on the performance of the Internet of vehicles [4], which enhances vehicle road safety and traffic management by effectively utilizing the underutilized computation resources of nearby vehicles and reduces traffic congestion and accidents, thus improving the network performance of the Internet of vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

Kang

Chen

Dai

2020

Wireless Communications and Mobile Computing

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A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

Kang

Chen

Dai

2020

Wireless Communications and Mobile Computing

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“…El Sibai et al [12, 13] proposed a connectivity-aware service provision mechanism, where the service provider vehicle is selected on the basis of several parameters such as the availability of the requested service and the mobility of the vehicles. This mechanism allows a service requester vehicle to form its own cloud.…”

Section: Related Workmentioning

confidence: 99%

A Vehicular Cloud-Based Framework for the Intelligent Transport Management of Big Cities

Meneguette

2016

International Journal of Distributed Sensor Networks

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An intelligent transport system (ITS) is intended to streamline the operations of vehicles, manage vehicle traffic, and help drivers with safety and other information, as well as supply convenient applications for passengers. This system is essential for tackling the problems of a big city, like traffic congestion and a lack of a communication infrastructure or traffic engineering, among other factors. With these challenges in mind, we propose a vehicular cloud architecture to assist in the management of large cities. This will create a framework to support different types of services as well as provide storage mechanisms, access, and information management which includes tools for different modes of transport not only for citizens but also for commercial vehicles and emergency services like ambulances. In addition, it will be possible to increase the capacity for abstraction to meet information needs through the use of vehicular networks and the integration of VANETs with other networks, so as to provide relevant information for the monitoring and management of an intelligent transport system.

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“…In the literature, many studies have proposed schemes to construct vehicular clouds by collaborating between vehicles in VANETs [12][13][14][15][16]. They are categorized into two approaches: single-hop and multiple-hop.…”

Section: Introductionmentioning

confidence: 99%

“…They are categorized into two approaches: single-hop and multiple-hop. The single-hop approach is to construct vehicular clouds by single-hop communications between vehicles [12].…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of Schemes for Replacing Multiple Member Vehicles in Vehicular Clouds

et al. 2022

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Vehicular clouds are considered an attractive approach to provide various services such as safety and entertainment applications by sharing resources between vehicles. Due to the free mobility of vehicles, vehicular clouds need to reconstruct by replacing leaving member vehicles with new member vehicles. When multiple member vehicles leave a vehicular cloud at different times, the design of an efficient member vehicle replacement scheme is a very challenging issue on determining the replacement timing. However, the research on the replacement of multiple member vehicles has little interest and is still in its infancy. Therefore, we propose three schemes to replace multiple member vehicles in vehicular clouds at three different replacement timings: MIN, MAX, and AVG. The MIN scheme replaces all of leaving member vehicles at the leaving time of the first leaving member vehicle, while the MAX scheme replaces all of leaving member vehicles at the leaving time of the last leaving member. The AVG scheme replaces all leaving member vehicles at the average time of their leaving times as a compromise between the Min and Max schemes. First, we determine the first leaving time, the last leaving time, and the average leaving time for each scheme by calculating the distance between a cloud requester vehicle and its member vehicles. Next, we choose replacement member vehicles to minimize the wasted resource at the replacement timing in each scheme. Last, we provide the process for releasing the resource of the leaving member vehicles and allocating the resource of the replacement member vehicles in each scheme. Through simulation results conducted in various environments, we compare and evaluate the performance of our three schemes in terms of the success ratio of the cloud maintenance and the amount of the wasted resources.

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Connectivity-aware service provision in vehicular cloud

Cited by 17 publications

References 6 publications

Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

A Vehicular Cloud-Based Framework for the Intelligent Transport Management of Big Cities

Design and Evaluation of Schemes for Replacing Multiple Member Vehicles in Vehicular Clouds

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