We present Mantis, a framework for predicting the computational resource consumption (CRC) of Android applications on given inputs accurately, and efficiently. A key insight underlying Mantis is that program codes often contain features that correlate with performance and these features can be automatically computed efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program's CRC metric yet can be evaluated efficiently from the program's input. We apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts four CRC metrics of seven Android apps with estimation error in the range of 0-11.1 percent by executing predictor code spending at most 1.3 percent of their execution time on Galaxy Nexus.

Recent efforts towards mobile cloud propose to offload mobile applications to cloud servers for the improved performance and battery life of mobile devices. However, existing schemes completely ignore the costs of cloud resources by assuming that idle servers are always available for free of charge. These unrealistic assumptions make each server run only a small load to achieve the guaranteed high offload performance. Therefore, these schemes cannot be applied to real-world commercial clouds which aim to minimize the operation costs by maximizing the server throughput, and then charge users for their resource usage. In this paper, we propose CMcloud, a novel cost-effective mobile-to-cloud offloading platform, which works nicely under the real-world cloud environments. CMcloud minimizes both the server costs and the user service fee by offloading as many mobile applications to a single server as possible, while satisfying the target performance of all applications. To achieve such goals, CMcloud exploits novel architecture performance modeling and server migration techniques. Our implementation shows that CMcloud can improve the data enter throughput by 84% over a conventional static light-load scheme (or a 2.7x higher per-socket throughput.) Alternatively, CMcloud reduces the number of service failures by 83% over a static high-load scheme, while even improving the throughput by 31%.

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer costs of every region. Expectedly, the transfer cost is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects and the stack frames actually referenced in the server. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and energy consumption.

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer time of every region. Expectedly, the transfer time is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and power consumption.