Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
复制标题
移动边缘计算卸载中资源分配的延迟优化
DOI：
10.1109/twc.2018.2845360
复制
发表时间：
2018-08-01
影响因子：
10.4
通讯作者：
He, Yinghui
中科院分区：
文献类型：
--
作者：
Ren, Jinke;Yu, Guanding;He, Yinghui
通过将密集的计算任务卸载到位于蜂窝基站的边缘云，移动边缘计算卸载（MECO）已被视为实现第五代网络雄心勃勃的毫秒级端到端延迟要求的有前途的手段。在本文中，我们研究了在多用户时分多址MECO系统的联合通信和计算资源分配的延迟最小化问题。研究了三种不同的计算模型，即，局部压缩、边缘云压缩和部分压缩卸载。首先，推导出局部和边缘云压缩模型的最优资源分配和最小系统延迟的闭合表达式。然后，对于部分压缩卸载模型，我们提出了一个分段优化问题，并证明了最优数据分割策略具有分段结构。在此基础上，提出了一种联合通信和计算资源的优化分配算法。为了获得更多的见解，我们还分析了一个特定的情况下，通信资源是足够的，而计算资源是有限的。在这种特殊情况下，可以导出分段优化问题的封闭形式解。最后通过数值实验验证了本文提出的算法的有效性，实验结果表明，新的部分压缩卸载模型能够显著降低端到端延迟。
By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.