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Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach

机译:移动云计算的动态计算分流:一种随机博弈论方法

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Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile user's offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.
机译:在移动应用程序日益普及的推动下,移动云计算已被视为提高移动设备计算能力并降低能耗的一种有前途的方法。在本文中,我们研究了动态环境下移动云计算的多用户计算卸载问题,其中移动用户动态变为活动或不活动,并且移动用户卸载计算的无线信道随机变化。由于移动用户对将计算任务转移到移动云上很感兴趣并且自私,因此我们将动态环境下移动用户的卸载决策过程表述为随机游戏。我们证明,公式化的随机博弈等于具有至少一个纳什均衡(NE)的加权潜在博弈。我们量化了NE的效率,并进一步提出了一种多智能体随机学习算法,以保证收敛速度(也可以通过分析得出)达到NE。最后,我们进行仿真以验证所提出算法的有效性并评估其在动态环境下的性能。

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