Nowadays, advanced smart mobile devices equipped with multi-core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single-thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi-core resources, this study proposes a fine-grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi-core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine-grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.

中文翻译：

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多核和云资源的协同调度：多线程应用程序的细粒度卸载策略

如今，先进的智能移动设备配备了用于处理多线程（MT）应用程序的多核中央处理器。但是，现有研究主要使用单线程（ST）计算来处理应用程序，这限制了移动计算的性能。为了充分利用多核资源，本研究提出了一种细粒度的MT卸载策略，以解决MT应用程序的卸载问题。该策略联合调度了云计算资源以及本地多核计算和通信资源。精确地，作者首先制定了ST卸载的最小能耗问题。然后，他们证明问题是凸的，并通过标准凸优化技术加以解决。第三，他们将优化目标从ST应用扩展到MT应用，为MT应用程序设计计算规则，以降低计算成本。最后，基于这些计算规则和ST卸载的最佳解决方案，他们开发了MT卸载策略来解决MT应用程序的计算卸载问题。仿真结果表明，提出的细粒度MT卸载策略有效地降低了移动计算的最小延迟需求。