Computation Offloading Scheme to Improve QoE in Vehicular Networks with Mobile Edge Computing

Liu, Qiaorong; Su, Zhou; Hui, Yilong

doi:10.1109/wcsp.2018.8555879

Cited by 19 publications

(9 citation statements)

References 15 publications

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“…MEC-enabled offloading have been proposed as a promising approach to solve the task offloading problems in recent research literatures [11][12][13][14][15]. In [11], authors proposed a joint task offloading and resource allocation algorithm in vehicular networks to minimize the cost of dualsides, which includes smart vehicle and the MEC server.…”

Section: Related Workmentioning

confidence: 99%

“…In [11], authors proposed a joint task offloading and resource allocation algorithm in vehicular networks to minimize the cost of dualsides, which includes smart vehicle and the MEC server. In [12], a distributed computation offloading scheme was proposed to optimize offloading decisions, while guaranteeing the quality of experience (QoE) of vehicles and maximizing the utility of the MEC server, where the utility consists of the energy consumption, delay, and computation resources. In [13], authors proposed a support vector machine-based offloading algorithm to reduce computation complexity, which ensures low latency in the offloading process for high-speed vehicles.…”

Section: Related Workmentioning

confidence: 99%

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Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang

Lai

et al. 2020

J Wireless Com Network

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Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang

Lai

et al. 2020

J Wireless Com Network

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show abstract

“…Roadside units play a vital role in vehicular edge computing networks. Liu, Q. et al [37] proposed an adequate disseminated computation offloading algorithm to enable better offloading arrangements for the vehicle to vehicle networks. To guarantee the most beneficial use of underutilized vehicular computational resources.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

et al. 2020

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Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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“…However, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. Moreover, extensive offloading may result in backbone network congestion [3].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Computation Offloading and Task Scheduling in Vehicular Edge Computing Networks

Sun

Zheng

et al. 2020

IEEE Access

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Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multiuser , multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations. INDEX TERMS Computation offloading, Internet of Things, mobile edge computing, task scheduling, vehicular networks.

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Computation Offloading Scheme to Improve QoE in Vehicular Networks with Mobile Edge Computing

Cited by 19 publications

References 15 publications

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Joint Optimization of Computation Offloading and Task Scheduling in Vehicular Edge Computing Networks

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