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The use of the internet of things (IoT) in the sector of intelligent vehicular transport systems is considered as the internet of vehicles. which covers connected vehicles, truck pantaloons, mobility services etc. Various services are needed through internet of vehicles such as traffic control, route planning information, and collision warning to make human traffic more convenient. However, in case of growing networks of large areas, a single server is incapable to handle the clients/ vehicles requests and the issue of load balancing with edge nodes remains unresolved. Increased computational latency, user mobility, and location - based services are still issues with the Internet of Vehicles (IoV). Here, we shows a software defined network based load balancing strategy for Internet of vehicle and minimization of latency and tasks of Internet of vehicles through cloud and edge computing devices using integrated whale optimization algorithm. The terminal user generate high traffic is the reason of increasing latency in cloud network. Software defined networking is the promising way of providing centralized controller and global knowledge to the cloud / IoV / Fog networking. The suggested model's performance is compared to the WOA, improved/ threshold whale optimization algorithm with respect to latency. Experimental analysis shows that the Internet of vehicle based load balancing in SDN using integrated Whale Optimization Algorithm works better than the other load balancing. Also, it efficiently minimizes the latency and improves the Quality of Service (QoS) in fog computing and enabling mobility and position awareness in IoT, Software Defined architecture. [ABSTRACT FROM AUTHOR]