Efficient Mobile Computation Offloading over a Finite-State Markovian Channel using Spectral State Aggregation

P. Teymoori, A. Boukerche, Feng Liang
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Abstract

This paper considers the problem of mobile computation offloading under stochastic wireless channels while task completion times are subject to deadline constraints. Our objective is to conserve energy for the mobile device by making an optimal decision to execute the task either locally or remotely. In the case of computation offloading, we dynamically vary the data transmission rate, in response to channel conditions. The wireless transmission channel is modelled using a Finite-State Markov Chain (FSMC). We formulate the problem of computation offloading as a constrained optimization problem, and develop an online algorithm to derive the optimal offloading policy. Moreover, to reduce the complexity, we estimate a suboptimal solution of the proposed online algorithm by reducing the size of the FSMC with the help of Markovian aggregation. The numerical results indicate that by applying Markovian aggregation, the running time of the algorithm can be significantly reduced without suffering unreasonable performance degradation.
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基于频谱状态聚合的有限状态马尔可夫信道的高效移动计算卸载
研究了随机无线信道下,任务完成时间受期限约束的移动计算卸载问题。我们的目标是通过做出本地或远程执行任务的最佳决策来为移动设备节省能量。在计算卸载的情况下,我们动态地改变数据传输速率,以响应信道条件。采用有限状态马尔可夫链(FSMC)对无线传输信道进行建模。我们将计算卸载问题表述为一个约束优化问题,并开发了一种在线算法来推导最优卸载策略。此外,为了降低复杂度,我们利用马尔可夫聚合方法减少FSMC的大小,估计出该在线算法的次优解。数值结果表明,采用马尔可夫聚合可以显著缩短算法的运行时间,而不会造成不合理的性能下降。
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