Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Danquah, Wiseborn Manfe; Altılar, D. Turgay

doi:10.1109/access.2020.3027637

Cited by 21 publications

(17 citation statements)

References 103 publications

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“…The execution time, total cost, average security score, and parallel efficiency for the different task-based partitioning and scheduling schemes shown in the tables are compared using the graphs in Figures 9,10,11,and 12. From the execution time graph in Figure 9, the values for the curves decrease and converge as more nodes are considered for task execution. The rate of decrease depends on the processor-to-channel capacity ratio (i.e., the ratio between the reciprocal processor speed (ω) and the inverse data transfer rate of the channel Z).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In order to efficiently provide the different cloud services, two main classes of vehicular cloud deployment models have been proposed in the literature: the peer-to-peer deployment model and the federated deployment model [12]. This classification depends on the type of services provided, the form of service access, the number of vehicles involved in the service provision, and the resource management scheme used.…”

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confidence: 99%

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UniDRM: Unified Data and Resource Management for Federated Vehicular Cloud Computing

Danquah¹,

Altılar

2021

IEEE Access

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The demand for computational resources in vehicular environments has increased due to the deployment of numerous intelligent transportation systems in the last decade. The federated vehicular cloud, a variant of vehicular cloud computing, can be considered as an emerging alternative for executing computationally intensive and delay-sensitive applications. However, the federated vehicular cloud is beset with a capacity-constrained communication channel and limited resource capacity in individual vehicles, which lead to data and resource management challenges. Therefore, we propose UniDRM, a unified data and resource management framework for the federated vehicular cloud, to address these challenges. The UniDRM organizes vehicles on the road into clusters based on their mobility and resource characteristics, such as resource cost, resource security level, resource type, and available resource capacity. The data of computationally intensive tasks are then partitioned using our proposed analytical model and assigned to individual vehicles in the cluster for parallel execution. Three partitioning and scheduling schemes: timeaware, cost-aware, and security-aware are proposed in this study to execute time-critical tasks, low-cost tasks, and high-security tasks, respectively. Through realistic simulations, a comparative analysis of the proposed partitioning and scheduling schemes is presented. INDEX TERMSVehicular cloud computing, federated vehicular cloud, resource management, resourcebased clustering, resource ranking, divisible load partitioning, divisible load scheduling I. INTRODUCTION

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Section: Performance Evaluationmentioning

confidence: 99%

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confidence: 99%

UniDRM: Unified Data and Resource Management for Federated Vehicular Cloud Computing

Danquah¹,

Altılar

2021

IEEE Access

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“…A vehicular cloud is defined as a set of vehicles capable of sharing their own resources such as computing, storage, and sensing resources [10]. In VANETs, a vehicle can construct a vehicular cloud by using the collection of vehicles' resources to enable a vehicular cloud service for a next-generation vehicular application [11].…”

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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“…This paper investigated how AI algorithms were utilized in vehicular resource allocation mechanisms. Through our literature study, several survey papers with similar themes [ 6 , 10 , 11 , 12 , 13 ] were found. In detail, the overall contribution of these similar survey papers can be seen in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Nurcahyani

Lee

2021

Sensors

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The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

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Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Cited by 21 publications

References 103 publications

UniDRM: Unified Data and Resource Management for Federated Vehicular Cloud Computing

UniDRM: Unified Data and Resource Management for Federated Vehicular Cloud Computing

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

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

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