Increasing the quality of services and resource utilization in vehicular cloud computing using best host selection methods

Sookhtsaraei, Reza; Iraji, Marzieh; Artin, Javad; Iraji, Mohammad Saber

doi:10.1007/s10586-020-03159-2

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…VCC signifies the latest hybrid technology, which includes a variety of network combinations like wireless sensor networks, VANET, and mobile ad hoc networks, [5,6] along with CC [7] [8]. VCC is employed for making vehicles work mutually to reach the storage together with the computational requirements of varied applications [9]. Vehicles exchange information in vehicle-vehicle communication without the support of roadside infrastructure [10].…”

Section: Introductionmentioning

confidence: 99%

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Kouser

Manikandan²

2023

Int J Communication

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Summary Various opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.

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