Geometric Programming for Lifetime Maximization in Mobile Edge Computing Networks
Geometric Programming for Lifetime Maximization in Mobile Edge Computing Networks
复制标题
DOI：
10.1109/globecom38437.2019.9013487
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Sabyasachi Gupta;Jacob Chakareski
中科院分区：
文献类型：
--
作者：
Sabyasachi Gupta;Jacob Chakareski
移动的边缘计算已经成为一种有前途的技术，以增强移动的设备的计算能力。针对多用户网络中的用户周期性地计算他们的任务的帮助下，边缘云，我们研究了网络寿命最大化问题的基础上，目前的用户任务信息。我们通过最小能效最大化（MEEM）策略来实现这一目标，该策略联合优化了卸载到云的用户任务计算的比例以及用户之间边缘计算和网络通信资源的相应分配。我们还研究了网络生命周期最大化的情况下，用户任务信息可用于所有未来的时隙，以及问题。此设置表示MEEM策略的上限。通过可行性测试和几何规划，制定两个调查策略的最佳解决方案。我们表明，MEEM可以实现70%的寿命提高了国家的最先进的和450%的寿命提高的情况下，本地用户任务计算。
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 450% lifetime improvement over the case of local user task computation only.