Distributed Threshold-based Offloading for Large-Scale Mobile Cloud Computing
Distributed Threshold-based Offloading for Large-Scale Mobile Cloud Computing
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DOI：
10.1109/infocom42981.2021.9488821
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Xudong Qin;Bin Li;Lei Ying
中科院分区：
文献类型：
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作者：
Xudong Qin;Bin Li;Lei Ying
移动云计算使计算能力有限的移动设备能够利用强大的云服务器执行实时密集型计算，例如语音识别或对象检测。大规模移动云计算的一个重要问题是计算卸载，每个移动设备通过考虑本地处理延迟和使用云服务器的成本来决定何时以及多少计算应该上传到云服务器。在本文中，我们开发了一种基于阈值的分布式卸载算法，如果设备上排队的任务数量达到阈值，则将传入的计算任务上传到云服务器，否则在本地进行处理。该阈值根据计算负载和使用云服务器的成本迭代更新。我们将问题表述为对称博弈，并在假设指数服务时间的情况下描述了纳什均衡（NE）存在和唯一性的充分必要条件。然后，我们展示了当 NE 存在时，我们提出的分布式算法对 NE 的收敛性。最后，我们进行了大量的模拟来验证我们的理论结果，并证明我们提出的分布式算法在各种实际场景下的效率，例如一般服务时间、不完美的服务器利用率估计和异步阈值更新。
Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold, and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Finally, we perform extensive simulations to validate our theoretical findings and demonstrate the efficiency of our proposed distributed algorithm under various practical scenarios such as general service times, imperfect server utilization estimation, and asynchronous threshold updates.