Lifetime Maximization in Mobile Edge Computing Networks
Lifetime Maximization in Mobile Edge Computing Networks
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DOI：
10.1109/tvt.2020.2965440
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发表时间：
2020-01
影响因子：
6.8
通讯作者：
Sabyasachi Gupta;Jacob Chakareski
中科院分区：
文献类型：
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作者：
Sabyasachi Gupta;Jacob Chakareski
移动边缘计算已成为一种颇具前景的技术，可增强移动设备的计算能力。对于一个多用户网络，其中用户借助边缘云定期计算其任务，我们基于当前用户任务信息研究网络寿命最大化问题。我们通过最小能效最大化（MEEM）策略来实现这一目标，该策略联合优化卸载到云端的用户任务计算比例，以及各用户间边缘计算和网络通信资源的分配。我们还研究了用户任务信息在所有未来时隙均已知的情况下的网络寿命最大化问题。此设定代表了MEEM策略的上限。通过可行性测试和几何规划，为所研究的两种策略制定了最优解。我们表明，与当前最先进的方法相比，MEEM可使网络寿命提高70%，与仅在本地进行用户任务计算的情况相比，可使网络寿命提高460%。我们还表明，对于完成用户计算任务的最大可容忍延迟的较高值，MEEM可实现全局最优的网络寿命性能。最后，我们表明，相对于当前最先进的方法，MEEM在不同网络拓扑结构下，可使网络寿命变化显著降低（降至三分之一）。
Mobile edge computing has emerged as a promising technology to augment the computational capabilities of mobile devices. For a multi-user network in which its users periodically compute their tasks with the help of an edge cloud, we investigate the network lifetime maximization problem based on present user task information. We pursue this objective via a minimum energy efficiency maximization (MEEM) strategy that jointly optimizes the fraction of user task computations offloaded to the cloud and the respective allocation of edge computing and network communication resources across the users. We also investigate the network lifetime maximization problem for the case when the user task information is available for all future time slots, as well. This setting represents an upper bound for the MEEM strategy. Optimal solutions for both investigated strategies are formulated via feasibility testing and geometric programming. We show that MEEM can achieve a 70% lifetime improvement over the state-of-the-art and 460% lifetime improvement over the case of local user task computation only. We also show that for a high value of the maximum tolerable delay for completing the computation tasks of the users, MEEM achieves the globally optimal network lifetime performance. Finally, we show that MEEM achieves a significant reduction (3X) in variation of enabled network lifetime over diverse network topologies, relative to the state-of-the-art.