Ultra-Low Latency Service Provision in Edge Computing

Luo, Jie; Deng, Xiaoheng; Zhang, Honggang; Qi, Huamei

doi:10.1109/icc.2018.8422645

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…After the comparisons, they find that the minimum load sharing scheme is most suitable for making full use of the cooperation among servers to realize the load balancing. Another study [ 22 ] proposed another edge computing task scheduling model, which transformed the waiting time minimization problem into an overall planning problem, and then carried out optimal scheduling through dynamic programming. A previous study [ 23 ] proposed an improved chaotic bat swarm algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…However, the proposed methods [ 12 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] result in unbalancing resource allocation for vehicular tasks due to dynamic network environment driven by humans. In order to achieve the load-balancing, literatures [ 29 , 30 ] used the idea of software defined network to obtain the more network status parameters.…”

Section: Related Workmentioning

confidence: 99%

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Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Peng

2021

Sensors

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With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Peng

2021

Sensors

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“…Then, the extra task offloading ratio b extra will be distributed to Vidle l+1 , Vidle l+2 , Vidle l+3 , • • • , Vidle N and the task vehicle. Similar to the above uniform distribution, the following equation holds: (26) where b equal2_i is the task workload assigned to Vidle i , i = l+ 1, l +2, • • • , N from b extra . The task offloading ratio assigned to Vidl i is:…”

Section: A the Optimal Solution For Special Casementioning

confidence: 99%

“…There has been considerable amount of work focusing on computation offloading [24]- [26]. The authors considered/analyzed the problem of binary offloading decision, where each user independently chooses whether to execute the task locally or to offload the whole task to the edge servers, to minimize the task execution time.…”

Section: Introductionmentioning

confidence: 99%

“…The authors considered/analyzed the problem of binary offloading decision, where each user independently chooses whether to execute the task locally or to offload the whole task to the edge servers, to minimize the task execution time. Studies, such as Luo et al [26] used dynamic programming and improved greedy algorithm to assign different tasks to multiple edge servers, to minimize the latency. Liu et al [27] formulated the multiple vehicles computation offloading problem as a multi-user computation offloading game problem, prove the existence of Nash equilibrium (NE) of the game and propose a distributed computation offloading algorithm to compute the equilibrium.…”

Section: Introductionmentioning

confidence: 99%

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Delay-Optimized V2V-Based Computation Offloading in Urban Vehicular Edge Computing and Networks

Chen

Liu

et al. 2020

IEEE Access

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. In view of the delay-sensitive property of these promising applications, as well as the high expense by using infrastructures and roadside units (RSU), the task offloading among vehicles has gained enormous popularity considering its free-of-charge and timely response. In this paper, by utilizing the gathering period of vehicles in urban environment due to stopped by traffic lights or Area of Interest (AOI), a task offloading scheme merely relying on vehicle-to-vehicle (V2V) communication is proposed by fully exploring the idle resources of gathered vehicles for task execution. Through formulating the task execution as a Min-Max problem among one task and several cooperative vehicles, the task executing time is optimized with the Max-Min Fairness scheme, which is further solved by the Particle Swarm Optimization (PSO) Algorithm. Extensive simulation demonstrate that our model could well meet the delay requirement of delay-sensitive application by cooperative computing among vehicles. INDEX TERMS Internet of vehicle, computing offloading, low latency, task allocation. LEI LIU (Student Member, IEEE) received the B.Eng. degree in communication engineering from Zhengzhou University, Zhengzhou, China, in 2010, and the M.Sc. degree in communication engineering from Xidian University, Xi'an, China, in 2013. He is currently pursuing the Ph.D. degree with Xidian University. He has published more than ten journals and conference publications. His research interests include vehicular ad hoc networks, intelligent transportation, mobile edge computing, and the Internet of Thing.

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Classification of optimization problems in fog computing

Bellendorf

Mann

2020

Future Generation Computer Systems

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Ultra-Low Latency Service Provision in Edge Computing

Cited by 13 publications

References 15 publications

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Delay-Optimized V2V-Based Computation Offloading in Urban Vehicular Edge Computing and Networks

Classification of optimization problems in fog computing

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