  Modern electric VUs are equipped with a variety of increasingly potent
computing, communication, and storage resources, and with this tremendous
computation power in their arsenal can be used to enhance the computing power
of regular cloud systems, which is termed as vehicular cloud. Unlike in the
traditional cloud computing resources, these vehicular cloud resource moves
around and participates in the vehicular cloud for a sporadic duration at
parking places, shopping malls, etc. This introduces the dynamic nature of
vehicular resource participation in the vehicular cloud. As the user-submitted
task gets allocated on these vehicular units for execution and the dynamic stay
nature of vehicular units, enforce the system to ensure the reliability of task
execution by allocating multiple redundant vehicular units for the task.
  In this work, we are maximizing the profit of vehicular cloud by ensuring the
reliability of task execution where user tasks come online manner with
different revenue, execution, and deadline. We propose an efficient approach to
solve this problem by considering (a) task classification based on the deadline
and laxity of the task, (b) ordering of tasks for task admission based on the
expected profit of the task, (c) classification of vehicular units based in
expected residency time and reliability concerned redundant allocation of tasks
of vehicular units considering this classification and (d) handing dynamic
scenario of the vehicular unit leaving the cloud system by copying the maximum
percentage of executed virtual machine of the task to the substitute unit. We
compared our proposed profit maximization approach with the state of art
approach and showed that our approach outperforms the state of art approach
with an extra 10\% to 20\% profit margin.
