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Modern electric VUs are equipped with a variety of increasingly potentcomputing, communication, and storage resources, and with this tremendouscomputation power in their arsenal can be used to enhance the computing powerof regular cloud systems, which is termed as vehicular cloud. Unlike in thetraditional cloud computing resources, these vehicular cloud resource movesaround and participates in the vehicular cloud for a sporadic duration atparking places, shopping malls, etc. This introduces the dynamic nature ofvehicular resource participation in the vehicular cloud. As the user-submittedtask gets allocated on these vehicular units for execution and the dynamic staynature of vehicular units, enforce the system to ensure the reliability of taskexecution by allocating multiple redundant vehicular units for the task.  In this work, we are maximizing the profit of vehicular cloud by ensuring thereliability of task execution where user tasks come online manner withdifferent revenue, execution, and deadline. We propose an efficient approach tosolve this problem by considering (a) task classification based on the deadlineand laxity of the task, (b) ordering of tasks for task admission based on theexpected profit of the task, (c) classification of vehicular units based inexpected residency time and reliability concerned redundant allocation of tasksof vehicular units considering this classification and (d) handing dynamicscenario of the vehicular unit leaving the cloud system by copying the maximumpercentage of executed virtual machine of the task to the substitute unit. Wecompared our proposed profit maximization approach with the state of artapproach and showed that our approach outperforms the state of art approachwith an extra 10\% to 20\% profit margin.

Efficient Profit Maximization in Reliability Concerned Static Vehicular Cloud System