Abstract

While the combination of cloud computing and mobile computing, termed mobile cloud computing, started to show its effects recently with many seemingly innovative smartphone applications and cloud services surfacing to the market today, we believe that the real potentials of mobile cloud computing is far from been fully explored due to several practical issues. The quality of the mobile networks is not adequate for delivering satisfactory user experiences via close collaboration over mobile networks. A dynamic workload partitioning scheme would help solve this problem, but the distribution of computation and data storage geographically can lead to serious security and privacy concerns, which makes user to take the risk of exposing the data to eavesdroppers in the middle of the network. Since we have yet to see collaborative mobile cloud applications which could dynamically migrate the workload to efficiently take advantage of the resources in the cloud, in this paper, we present a paradigm to guide the design of the following: the system architecture, the principle for partitioning applications, the method for offloading computation, and the control policy for data access. We argue that the proposed paradigm provides a unified solution to the performance and privacy issues, with a case study, a cloud-assisted speech recognition application, to illustrate our experimental results.