In the previous couple of years, various types of researchers concentrate on Vehicular Ad-hoc networks (VANET) field due to various facilities it provides. VANET a subgroup of mobile ad-hoc network (MANET), refers to a group of intelligent nodes i.e. (vehicles) on the road. These intelligent vehicles interact with one other or with the road side unit (RSU) for providing safer roads and a more efficient driving experience and providing security against attackers. In VANET messages are conveyed in an open wireless channels Security is therefore the most concerning issue in VANET. In this paper several types of the security issues, requirements, attacks, attackers in VANET have been described and some recent solutions to solve the security problems with their advantages and disadvantages have been discussed.

Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost.