Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang, Minghui; Hosseinalipour, Seyyedali; Gao, Zhibin; Tang, Yuliang; Huang, Lianfen; Dai, Huaiyu

doi:10.1109/jiot.2019.2949602

Cited by 45 publications

(32 citation statements)

References 28 publications

(48 reference statements)

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“…M. LiWang et al [22] proposed a randomized graph job allocation mechanism using hierarchical tree-based subgraph isomorphism with low complexity. However, the graph-based operation mechanism cannot adapt to the rapid changes in topological structure of the Internet of Vehicles environment.…”

Section: Related Workmentioning

confidence: 99%

OPTOS: A Strategy of Online Pre-Filtering Task Offloading System in Vehicular Ad Hoc Networks

Wang

et al. 2022

IEEE Access

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The advanced services provided by vehicle ad hoc network (VANET) often require vehicles to process complex computing tasks that may not be completed by individual vehicles within a required delay limit. Task offloading to road side units (RSUs) is a typical approach to enhancing service performance in VANET; however, RSUs may not always have sufficient resources for handling all task offloading requests. With the increasing amount of computing capacities available on vehicles, offloading tasks to other vehicles offers a promising alternative to RSU-based task offloading. However, vehicle-to-vehicle task offloading in VANET faces some new challenges that have not been fully addressed, among which is the degraded delay performance caused by vehicle mobility. In order to solve this problem, we propose an Online Pre-filtering Task Offloading System (OPTOS) that is able to mitigate the impact of vehicle mobility on task offloading performance. OPTOS comprises a process that selects candidate vehicles for hosting offloaded tasks and a HGSA algorithm that assigns tasks to vehicles for minimizing task completion delay while balancing utilization of computing capacities on different vehicles. We have conducted extensive experiments using a real-world dataset for evaluating the performance of the proposed OPTOS. Obtained results indicate that OPTOS is effective for reducing task completion delay and increasing task success rate in various VANET scenarios with different levels of vehicle mobility.INDEX TERMS Task offloading, Vehicular ad hoc network (VANET), Vehicular-to-vehicular (V2V), Vehicle mobility

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Section: Related Workmentioning

confidence: 99%

OPTOS: A Strategy of Online Pre-Filtering Task Offloading System in Vehicular Ad Hoc Networks

Wang

et al. 2022

IEEE Access

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“…M. LiWang et al [22] studied the allocation problem of V2V communication in vehicular cloud environment. They proposed a randomized graph job allocation mechanism via hierarchical tree-based subgraph isomorphism with low complexity.…”

Section: B V2v Task Offloadingmentioning

confidence: 99%

“…Moreover, these vehicles form a distributed structure, and tasks can be executed by different in parallel. In recent years, some researchers have also shifted their focus to vehicleto-vehicle offloading [22]- [27]. Tasks are classified into dependent tasks and independent tasks according to whether they need to be executed in a fixed order.…”

Section: Introductionmentioning

confidence: 99%

V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks

He¹,

Wang²,

Du³

et al. 2021

Preprint

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In the research and application of vehicle ad hoc networks (VANETs), it is often assumed that vehicles obtain cloud computing services by accessing to roadside units (RSUs). However, due to the problems of insufficient construction quantity, limited communication range and overload of calculation load of roadside units, the calculation mode relying only on vehicle to roadside units is difficult to deal with complex and changeable calculation tasks. In this paper, when the roadside unit is missing, the vehicle mobile unit is regarded as a natural edge computing node to make full use of the excess computing power of mobile vehicles and perform the offloading task of surrounding mobile vehicles in time. In this paper, the OPFTO framework is designed, an improved task allocation algorithm HGSA is proposed, and the pre-filtering process is designed with full consideration of the moving characteristics of vehicles. In addition, vehicle simulation experiments show that the proposed strategy has the advantages of low delay and high accuracy compared with other task scheduling strategies, which provides a reference scheme for the construction of Urban Intelligent Transportation in the future.

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“…1, which contains two participants: the seller (MEC server) and the buyer (vehicle). The seller owns a collection of machines [23], [24] where each machine has a set of virtual machines (VMs) that can run a certain number of tasks in parallel. Specifically, VMs and tasks are matched to each other following one-to-one mode.…”

Section: Framework Of Futures-based Resource Trading In Ec-iovmentioning

confidence: 99%

Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach

Liwang

Chen

Xian-bin

2021

Preprint

Self Cite

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Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, the onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futuresbased resource trading approach in edge computing-enabled internet of vehicles (EC-IoV), where a forward contract is used to facilitate resource trading related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties in the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings considerable utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.

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Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Cited by 45 publications

References 28 publications

OPTOS: A Strategy of Online Pre-Filtering Task Offloading System in Vehicular Ad Hoc Networks

OPTOS: A Strategy of Online Pre-Filtering Task Offloading System in Vehicular Ad Hoc Networks

V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks

Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach

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