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To Offload or to Wait: An Opportunistic Offloading Algorithm for Parallel Tasks in a Mobile Cloud

机译:卸载还是等待:移动云中并行任务的机会卸载算法

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The significant development of mobile cloud computing allows a mobile user to access resources of the nearby mobile devices, i.e., Cloudlets, for processing tasks by using the offloading mechanism. However, due to the mobility of the user and cloudlets, the connection between the user's device and cloudlets may be interrupted since cloudlets move out of transmission range of the user's device. Consequently, the task transmission may fail, forcing the user to re-offload the task to another cloudlet or process on the local device. In this paper, we propose a dynamic opportunistic offloading algorithm which allows the user to make the decision of offloading or deferring the processing of each task in a set of parallel tasks. We formulate and solve a Markov Decision Process (MDP) model for the mobile user to obtain an optimal offloading policy while minimizing the offloading and processing cost. We extend the MDP model to a constrained MDP to solve the offloading problem when the user has a processing deadline. Numerical studies and simulations were carried out to evaluate the performance of the proposed model. The results show that the proposed model outperforms conventional baseline schemes.
机译:移动云计算的重大发展允许移动用户使用卸载机制来访问附近的移动设备(即Cloudlets)的资源,以处理任务。但是,由于用户和小云的移动性,由于小云移出用户设备的传输范围,因此用户设备和小云之间的连接可能会中断。因此,任务传输可能会失败,从而迫使用户将任务重新分流到本地设备上的另一个cloudlet或进程。在本文中,我们提出了一种动态机会卸载算法,该算法使用户可以决定卸载还是推迟一组并行任务中每个任务的处理。我们为移动用户制定并解决了马尔可夫决策过程(MDP)模型,以在减少卸载和处理成本的同时获得最佳卸载策略。我们将MDP模型扩展为受约束的MDP,以解决用户有处理期限的卸载问题。进行了数值研究和仿真,以评估所提出模型的性能。结果表明,所提出的模型优于传统的基线方案。

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