Mobile cloud computing has been widely used to support the computation-intensive applications in mobile devices. Since cloud computing relies on the facilities like network infrastructures and cloud servers, the cloud service can be easily hampered by the limitation of these facilities in many cases. Therefore, researchers proposed the concept of device-to-device offloading to offload workload to nearby devices, without going through other facilities. Most existing works on device-to-device offloading aim to minimize the execution time of tasks or minimize the energy consumption. These works assume devices can be connected for a long time, but they neglect the fact that the connections among mobile devices are usually intermittent and even opportunistic, which may render the failure of task offloading. In this paper, we propose a new approach, PeerCloud, that aims to improve the success ratio of task offloading. Specifically, since mobile devices are carried by human beings, we study the social relationship between the device carriers and discover the hidden regularity in their contact patterns, in order to predict the likelihood of node departure. Optimization problems are then formalized to maximize the success ratio of task offloading within tasks’ time constraints. Experimental results based on three real-world datasets demonstrate that PeerCloud outperforms existing approaches by significantly improving the success ratio.

移动云计算已被广泛用于支持移动设备中的计算密集型应用程序。由于云计算依赖于诸如网络基础结构和云服务器之类的设施，因此在许多情况下，这些设施的局限性很容易妨碍云服务。因此，研究人员提出了设备到设备卸载的概念，以将工作负载卸载到附近的设备，而无需经过其他设施。现有的大多数关于设备到设备卸载的工作旨在最小化任务的执行时间或最小化能耗。这些工作假定设备可以长时间连接，但是他们忽略了以下事实：移动设备之间的连接通常是断断续续的，甚至是机会主义的，这可能导致任务卸载失败。在本文中，我们提出了一种新的方法，PeerCloud，旨在提高任务卸载的成功率。具体来说，由于移动设备是由人类携带的，因此我们研究了设备载体之间的社会关系，并发现了它们的联系方式中隐藏的规律性，以便预测节点偏离的可能性。然后将优化问题形式化，以在任务的时间限制内最大化任务卸载的成功率。基于三个真实数据集的实验结果表明，PeerCloud通过显着提高成功率，胜过现有方法。为了预测节点偏离的可能性。然后将优化问题形式化，以在任务的时间限制内最大化任务卸载的成功率。基于三个真实数据集的实验结果表明，PeerCloud通过显着提高成功率，胜过现有方法。为了预测节点偏离的可能性。然后将优化问题形式化，以在任务的时间限制内最大化任务卸载的成功率。基于三个真实数据集的实验结果表明，PeerCloud通过显着提高成功率，胜过现有方法。