论文标题
用于多电池移动边缘计算的两次计算方法
A Two-Timescale Approach to Mobility Management for Multi-Cell Mobile Edge Computing
论文作者
论文摘要
移动边缘计算(MEC)是一项有前途的技术,可通过将复杂的计算任务卸载到边缘服务器,从而增强移动用户的计算能力和功能。但是,移动性在提供关键延迟应用所需的可靠MEC服务方面构成了巨大挑战。首先,移动性管理必须解决在不同时间范围内不同的位置更改和任务到达的动态。其次,用户移动性可能导致服务迁移,从而导致可靠性损失,这是由于迁移延迟而导致的。在本文中,我们通过共同控制服务迁移和传输能力来解决以上挑战的共同控制,提出了一个两次计算的移动性管理框架。具体而言,服务迁移在大的时间范围内运行,以支持多单元网络中的用户移动性,而电源控制则是在小时范围内执行的,以实时任务下载。他们的联合控制被提出为优化问题,旨在遵守计算卸载的可靠性要求的长期移动能量最小化。为了解决问题,我们提出了一个基于Lyapunov的框架,将问题分解为不同的时间尺度,基于该框架,通过利用问题结构来开发低复杂性的两次在线算法。提出的在线算法通过理论分析表明是渐近的最佳选择,并进一步开发以适应多源管理。模拟结果表明,我们提出的算法可以显着提高能量和可靠性性能。
Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering reliable MEC service required for latency-critical applications. First, mobility management has to tackle the dynamics of both user's location changes and task arrivals that vary in different timescales. Second, user mobility could induce service migration, leading to reliability loss due to the migration delay. In this paper, we propose a two-timescale mobility management framework by joint control of service migration and transmission power to address the above challenges. Specifically, the service migration operates at a large timescale to support user mobility in the multi-cell network, while the power control is performed at a small timescale for real-time task offloading. Their joint control is formulated as an optimization problem aiming at the long-term mobile energy minimization subject to the reliability requirement of computation offloading. To solve the problem, we propose a Lyapunov-based framework to decompose the problem into different timescales, based on which a low-complexity two-timescale online algorithm is developed by exploiting the problem structure. The proposed online algorithm is shown to be asymptotically optimal via theoretical analysis, and is further developed to accommodate the multiuser management. The simulation results demonstrate that our proposed algorithm can significantly improve the energy and reliability performance.