Network Variation and Fault Tolerant Performance Acceleration in Mobile Devices with Simultaneous Remote Execution

As mobile applications provide increasingly richer features to end users, it has become imperative to overcome the constraints of a resource-limited mobile hardware. Remote execution is one promising technique to resolve this important problem. Using this technique, the computation intensive part of the workload is migrated to resource-rich servers, and then once the computation is completed, the results can be returned to the client devices. To enable this operation, strong wireless connectivity is required. However, unstable wireless connections are the staple of real-life. This makes performance unpredictable, sometimes offsetting the benefits brought by this technique and leading to performance degradation. To address this problem, in this paper, we present a Simultaneous Remote Execution (SRE) model for mobile devices. Our SRE model performs concurrent executions both locally and remotely. Therefore, the worst-case execution time on fluctuating network condition is significantly reduced. In addition, SRE provides inherent tolerance for abrupt network failure. We designed and implemented an SRE-based offloading system consisting of a real smartphone and a remote server connected via 3G and Wifi networks. The experimental results under various real-life network variation scenarios show that SRE outperforms the alternative schemes in highly fluctuating network environments.