Although recent developments in the hardware of mobile devices, such as processor and memory capacity have increased their capabilities, they are still not comparable to cloud servers. The capacity constraints of mobile devices can be overcome by having the computing intensive work of mobile applications performed on powerful local or cloud servers. One of the important aspects of computation offloading is the decision process; this is determined by the costs of running the computation intensive components at run time on the server or at the local. This study proposes a novel hybrid model. An object dependency graph was created by gathering data from the mobile device at run time. This graph was partitioned with a novel model to determine the offloadable parts, which were then sent to the server using an online learning algorithm. Mobile applications were implemented on Android OS to verify the hybrid model. Properly making the offloading decision improved the application performance and decreased the battery consumption. Our algorithm has yielded better results than existing studies. The response time was saved by 2–73% and energy was reduced by 16–44% through offloading the computation intensive parts of mobile applications.

中文翻译：

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使用在线学习算法为移动应用程序进行无缝计算卸载

尽管移动设备硬件的最新发展，例如处理器和内存容量，它们的功能有所增加，但它们仍然无法与云服务器相媲美。通过在强大的本地或云服务器上执行移动应用程序的计算密集型工作，可以克服移动设备的容量限制。计算卸载的重要方面之一是决策过程；这取决于在服务器或本地运行时运行计算密集型组件的成本。本研究提出了一种新的混合模型。通过在运行时从移动设备收集数据来创建对象依赖关系图。该图用一个新颖的模型进行分区，以确定可卸载的部分，然后使用在线学习算法将其发送到服务器。移动应用程序在 Android 操作系统上实现以验证混合模型。正确做出卸载决策可提高应用程序性能并降低电池消耗。我们的算法产生了比现有研究更好的结果。通过卸载移动应用程序的计算密集型部分，响应时间节省了 2-73%，能源减少了 16-44%。