Abstract

Vehicular clouds (VCs) have become a promising research area due to its on-demand solutions, resource pooling, unified services, autonomous cloud formation and transformational management. It makes use of the underutilized resources of vehicles on the parking lot, roadways, driveways and streets, and creates the infrastructure to support various services offered by the cloud service provider (CSP) by deploying virtual machines (VMs). However, these vehicles can leave the coverage/grid of VC due to its mobility and change in the environment. Therefore, the hosted VMs on those vehicles can be transferred to other potential vehicles (i.e., migration) in order to avoid disruption of services. These services can be viewed as user requests (URs) submitted to the CSP by cloud users. Here, the challenging tasks are to map the URs to the VMs (or vehicles) and identify the potential vehicles for migration, and they need immediate attention. In this paper, we propose a smart cloud service management (SCSM) algorithm for VCs and address the above challenges. This algorithm consists of three phases, namely assignment of vehicles to grids, URs to grids and URs to vehicles by considering the mobility pattern of vehicles. The performance of SCSM is assessed using three traffic congestion scenarios and thirty-six instances of four datasets, and compared with round-robin (RR) and deficit weighted RR (DWRR) using seven performance metrics. The comparison results show that SCSM achieves 58% and 57% (33% and 33%) better than RR and DWRR in makespan (number of migrations) and other performance metrics.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.