Efficient Resource management in an Vehicular Cloud Networks (VCN) results in an increase resource utilization and reduction of the cost. Proper resource allocation schemes in VCN provides the better performance in terms the reduction of cost, reduction in the waiting time of vehicle (client) and also the waiting queue length. Resources are required to provide more efficiently by the cloud providers for the requested services by the vehicle. For this reason it is necessary to design proper resource allocation schemes in VCN. The aim of resource allocation scheme in VCN is to allocate the appropriate computing resources for the client vehicle application. Efficient resource allocation scheme in VCN plays a major role in the overall performance of the system. Members of VCN change dynamically due to the mobility in their movement. Vehicles may face high costs or issue related to the performance parameter when proper resource allocation schemes are not applied. In this work, we proposed the cost effective based resource allocation in VCN. The proposed cost model provides the resource to vehicle by considering the lesser expensive approach hence by achieving in the reduction of cost. We compare the results of the cost optimization with the generic algorithm that uses a combination of best fit and first fit techniques for resource allocation in VCN.

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Cost-Aware Task Scheduling in Cloud Computing Environment

Mokhtar A. Alworafi, Atyaf Dhari, Asma A. Al-Hashmi, Suresha, A. Basit Darem

Статья научная

Cloud computing is a new generation of computing environment which delivers the applications as a service to users over the internet. The users can select any service from a list provided by service providers depending on their demands or needs. The nature of this new computing environment leads to tasks scheduling and load balancing problems which become a booming research area. In this paper, we have proposed Scheduling Cost Approach (SCA) that calculates the cost of CPU, RAM, bandwidth, storage available. In this approach, the tasks will be distributed among the VMs based on the priority given by user. The priority depends on the user budget satisfaction. The proposed SCA will try to improve the load balance by selecting suitable VM for each task. The results of SCA are compared with the results of FCFS and SJF algorithms which proves that, the proposed SCA approach significantly reduces the cost of CPU, RAM, bandwidth, storage compared to FCFS and SJF algorithms.

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