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Energy Management for Multi-User Mobile-Edge Computing Systems with Energy Harvesting Devices and QoS Constraints

机译:具有能量收集设备和QoS约束的多用户移动边缘计算系统的能量管理

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Mobile-edge computing (MEC) has evolved as a promising technology to alleviate the computing pressure of mobile devices by offloading computation tasks to MEC server. Energy management is challenging since the unpredictability of the energy harvesting and the quality of service (QoS). In this paper, we investigate the problem of power consumption in a multi-user MEC system with energy harvesting (EH) devices. The system power consumption, which includes the local execution power and the offloading transmission power, is designated as the main system performance index. First, we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming, which is difficult to solve due to the time-coupling constraints. Then, we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints.We design an online algorithm based on the Lyapunov optimization method, which only uses current states of the mobile users (MUs) and does not depend on the system statistic information. Moreover, we prove the optimality of the online algorithm using rigorous theoretical analysis. Finally, we perform extensive trace-simulations to verify the theoretical results and evaluate the effectiveness of the proposed algorithms.
机译:移动边缘计算(MEC)已经发展成为一种有前途的技术,可以通过将计算任务卸载到MEC服务器来减轻移动设备的计算压力。能量管理具有挑战性,因为能量收集和服务质量(QoS)不可预测。在本文中,我们研究了带有能量收集(EH)设备的多用户MEC系统中的功耗问题。包括本地执行功率和卸载传输功率在内的系统功耗被指定为主要系统性能指标。首先,我们将具有电池队列稳定性和QoS约束的功耗最小化问题表示为随机优化程序,由于时间耦合约束,该问题难以解决。然后,我们采用Lyapunov优化方法将其重新构造为具有宽松队列稳定性约束的问题来解决该问题。基于Lyapunov优化方法设计一种在线算法,该算法仅使用移动用户(MU)的当前状态并执行不依赖于系统统计信息。此外,我们通过严格的理论分析证明了在线算法的最优性。最后,我们进行了广泛的跟踪仿真,以验证理论结果并评估所提出算法的有效性。

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