A Distributed Algorithm for Task Offloading in Vehicular Networks With Hybrid Fog/Cloud Computing

2021 ◽  
pp. 1-14
Author(s):  
Zongkai Liu ◽  
Penglin Dai ◽  
Huanlai Xing ◽  
Zhaofei Yu ◽  
Wei Zhang
Keyword(s):  
Cloud Computing ◽  
Distributed Algorithm ◽  
Vehicular Networks ◽  
Task Offloading

Game Theory-Based Task Offloading and Resource Allocation for Vehicular Networks in Edge-Cloud Computing

2021 ◽  
Author(s):  
Qinting Jiang ◽  
Xiaolong Xu ◽  
Qiang He ◽  
Xuyun Zhang ◽  
Fei Dai ◽  
...  
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Vehicular Networks ◽  
Task Offloading

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

Vehicular Cloud Computing

2014 ◽  
pp. 262-274 ◽  
Author(s):  
Kayhan Zrar Ghafoor ◽  
Marwan Aziz Mohammed ◽  
Kamalrulnizam Abu Bakar ◽  
Ali Safa Sadiq ◽  
Jaime Lloret
Keyword(s):  
Cloud Computing ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Performance Improvements ◽  
Potential Applications ◽  
Research Outcome ◽  
Hoc Networks

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

Vehicular Clouds

2018 ◽  
pp. 1-29
Author(s):  
Ryan Florin ◽  
Stephan Olariu
Keyword(s):  
Cloud Computing ◽  
Recent Work ◽  
Vehicular Networks ◽  
Research Community ◽  
Active Area ◽  
Vehicular Cloud ◽  
The Future

Vehicular clouds is an active area of research that has emerged at the nexus of conventional cloud computing and vehicular networks. The defining differences between conventional and vehicular clouds include the heterogeneity and volatility of compute resources and the bandwidth-challenged network fabric. A variety of new architectures and services for vehicular clouds have been proposed, mostly as incremental extensions of the VANET platform. As vehicular cloud research continues and expands, a careful eye should be kept on the restrictions that come with the mobility, limited network, and heterogeneity of resources. The first main contribution of this chapter is to survey recent work of VCs with an eye on the realistic and unrealistic. Our second main goal is to realign the VC community with a realistic vision for the future by spelling out a number of challenges faced by the VC research community.

scholarly journals New Application Task Offloading Algorithms for Edge, Fog, and Cloud Computing Paradigms

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Sungwook Kim
Keyword(s):  
Cloud Computing ◽  
Superior Performance ◽  
Bargaining Solution ◽  
Simulation Studies ◽  
Research Issues ◽  
Application Service ◽  
Primary Advantage ◽  
Strategic Solution ◽  
Future Work ◽  
Task Offloading

In the last few years, we have seen an exponential increase in the number of computation-intensive applications, which have resulted in the popularity of fog and cloud computing paradigms among smart-chip-embedded mobile devices. These devices can partially offload computation tasks either using the fog system or using the cloud system. In this study, we design a new task offloading scheme by considering the challenges of future edge, fog and cloud computing paradigms. To provide an effective solution toward an appropriate task offloading problem, we focus on two cooperative bargaining game solutions—Tempered Aspirations Bargaining Solution (TABS) and Gupta-Livne Bargaining Solution (GLBS). To maximize the application service quality, a proper bargaining solution should be properly selected. In the proposed scheme, the TABS method is used for time-sensitive offloading services, and the GLBS method is applied to ensure computation-oriented offloading services. The primary advantage of our bargaining-based approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current network environments. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme, and the superior performance over existing schemes is observed. Finally, we show prime directions for future work and potential research issues.

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