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.

翻译：现代电动车辆单元配备了日益强大的计算、通信和存储资源，这些丰富的计算能力可用于增强传统云系统的计算能力，即所谓车联云。与传统云计算资源不同，这些车联云资源会移动，并在停车场、购物中心等地点以零散时长参与车联云，这引入了车辆资源参与车联云的动态特性。由于用户提交的任务被分配到这些车辆单元上执行，而车辆单元具有动态停留特性，这迫使系统通过为任务分配多个冗余车辆单元来确保任务执行的可靠性。本研究通过保证任务执行可靠性来最大化车联云利润，其中用户任务以在线方式到达，且具有不同的收益、执行时间和截止时间。我们提出了一种高效方法来解决该问题，该方法包括：(a) 基于任务的截止时间和松弛度进行任务分类，(b) 基于任务预期利润对任务进行排序以决定任务准入，(c) 基于预期驻留时间对车辆单元进行分类，并依据此分类进行面向可靠性的任务冗余分配，以及(d) 通过将任务已执行虚拟机的最大百分比复制到替代单元，来处理车辆单元离开云系统的动态场景。我们将所提出的利润最大化方法与现有最优方法进行了比较，结果表明，我们的方法超越了现有最优方法，额外获得了10%至20%的利润空间。