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.
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