Optimal job partitioning and allocation for vehicular cloud computing

Kim, Tae-Sik; Min, Hong; Choi, Eunsoo; Jung, Jinman

doi:10.1016/j.future.2020.02.007

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…However, the MapReduce method does not explicitly consider vehicular nodes' frequent departure and joining in the vehicular cloud. This leads to higher waiting times when transmitting the results of partitioned tasks [25].…”

Section: Motivation and Related Workmentioning

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: Motivation and Related Workmentioning

confidence: 99%

“…An optimal partitioning and allocation scheme based on the dynamic characteristics of vehicular cloud resources was proposed by [25]. In the study, the arrival and departure rates of vehicles in a parking lot were considered to model a distribution to determine the available resource capacity of vehicles.…”

Section: Motivation and Related Workmentioning

confidence: 99%

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

Danquah¹,

Altılar

2021

IEEE Access

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“…al. [86] proposed a large scale job partitioning and allocation algorithm. The algorithm determines the optimal number of tasks a job can be divided into and distributed to vehicles to minimize the overall job execution time.…”

Section: ) Vehicular Cloud Resource Brokering Systemmentioning

confidence: 99%

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Danquah

Altılar

2020

IEEE Access

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Recent advancements in the automotive industry have led to the design of smart vehicles with high capacity resources for communication, sensing, processing, and storage of data. In the near future, it is envisaged that these resources will be harnessed and utilized to provide cloud services such as storage as a service, computation as a service, and sensing as a service. This paradigm of computing termed vehicular cloud, presents a lot of opportunities for the deployment of delay-sensitive applications in vehicular environment. However, high mobility of vehicles and rapidly changing topology of vehicular networks introduce new challenges such as instability of resources which make the management of resources in vehicular cloud complicated. Therefore, vehicular cloud computing require robust and mobility-aware resource management solutions to be designed. In order to achieve this goal, researchers and engineers need to be abreast with the techniques of resource management in vehicular cloud. This paper presents a detailed survey of resource management tasks in vehicular clouds. A thorough introduction to vehicular cloud is presented initially. Then we identify and examine all the resource management tasks that are carried out in vehicular cloud by classifying them into three different phases: pre-resource assignment phase, resource-assignment phase, and post resource assignment phase. Proposed solutions to resource management challenges in literature at each phase are reviewed in detail. Unlike the existing surveys on vehicular clouds, this study covers all aspects of resource management such as resource brokering, resource demand prediction, resource allocation, and resource scheduling. Following the extensive survey, we also propose frameworks for resource monitoring and resource brokering for vehicular cloud. Finally, open issues and research directions for vehicular cloud resource management are presented.

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“…In [29], authors have suggested a mixed ILP for VC to assign resources to the tasks. For the efficient utilisation of resources, a job partition method was discussed in [30]. Kim et al [30] have proposed an optimal job partitioning and allocation algorithm for large-scale jobs in the VC.…”

Section: Related Workmentioning

confidence: 99%

“…For the efficient utilisation of resources, a job partition method was discussed in [30]. Kim et al [30] have proposed an optimal job partitioning and allocation algorithm for large‐scale jobs in the VC. These resource allocation strategies have not considered the dynamic nature of VCC.…”

Section: Related Workmentioning

confidence: 99%

FRAC: a flexible resource allocation for vehicular cloud system

Pradhan

Tripathy

2020

IET intell. transp. syst

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Vehicular cloud computing (VCC) is a promising technology for the intelligent transport system. It provides a better quality of transport and vehicular services that will increase the safety and comfort of drivers and passengers. A group of vehicles creates VCC and offers vehicular services for its users in the absence of infrastructure to support the system. The vehicles having computing, storage, communication, and sensing devices can share these resources with other vehicles. VCC creates a resource pool by aggregating all the shared resources of nearby vehicles. The major challenge for VCC is to manage the resources from different vehicles in a resource pool and to allocate necessary resources to its user on-demand. The authors propose a semi-Markov decision process based resource allocation method for the VCC system called flexible resource allocation for vehicular cloud system to manage and allocate the resources. The proposed method finds optimal resource allocation strategies for different states of the VCC system and maximises the long-term expected reward under different parameter settings. Nomenclature K maximum number of vehicles that the VC can support v the vehicle ID R type of resources in a vehicle λ p arrival rate of a request of priority p M i maximum available resource of type i n i number of RUs of type i that VC can allocate to a request λ Req rate of arrival of request μ Req departure rate of the request λ v arrival rate of vehicle μ v departure rate of vehicle ξ gain for the system, when a vehicle arrives ξ −1 penalty for system when a vehicle leaves τ Req gain when a job completes and request departs Delay Req energy lost when a request blocked

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Optimal job partitioning and allocation for vehicular cloud computing

Cited by 18 publications

References 27 publications

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

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

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

FRAC: a flexible resource allocation for vehicular cloud system

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