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Abstract
Cloud computational platform provisions numerous cloud-based Vehicular Adhoc Network (VANET) applications. For providing better bandwidth and connectivity in dynamic manner, Software Defined VANET (SDVN) is developed. Using SDVN, new VANET framework are modeled; for example, Software Defined Vehicular Cloud (SDVC). In SDVC, the vehicle enables virtualization technology through SDVN and provides complex data-intensive workload execution in scalable and efficient manner. Vehicular Edge Computing (VEC) addresses various challenges of fifth generation (5G) workload applications performance and deadline requirement. VEC aid in reducing response time, delay with high reliability for workload execution. Here the workload tasks are executed to nearby edge devices connected to Road Side Unit (RSU) with limited computing capability. If the resources are not available in RSU, then the task execution is offloaded through SDN toward heterogeneous cloud server. Existing workload scheduling in cloud environment are designed considering minimizing cost and delay; however, very limited work has been done considering energy minimization for workload execution. This paper presents a Load Balanced and Energy Aware Cloud Resource Scheduling (LBEACRS) design for heterogeneous cloud framework. Experiment outcome shows the LBEACRS achieves better makespan and energy efficiency performance when compared with standard cloud resource scheduling design.
Highlights
As of late, its seen that the amount of Internet associated gadgets are more when compared with the quantity of people in the world, Internet-associated gadgets are expected to exceed thirty billion by 2020, as per report suggested in [1], accidentally apprehending the IoT models
This paper presents a load balanced and energy aware cloud resource scheduling method for executing dataintensive workload on software defined vehicular cloud (SDVC)
Experiment is conducted for evaluating LBEACRS algorithm methodology over standard cloud resource scheduling algorithm [11], [12]
Summary
Its seen that the amount of Internet associated gadgets are more when compared with the quantity of people in the world, Internet-associated gadgets are expected to exceed thirty billion by 2020, as per report suggested in [1], accidentally apprehending the IoT models. As there is an enormous increase in the vehicles which are connected to the internet, the normal VANETs are merged into these Internet of Vehicles (IoV). The restricted capability for the computation and the because of less resource’s capacity in the vehicles, it is a challenging task for the decision-making, networking and data processing in real-time. Due to this problem, it develops an issue for computing the data and allocating the resources to the different applications in the VANETs which have limited resources
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