QLRan: Latency-Quality Tradeoffs and Task Offloading in Multi-node Next Generation RANs
QLRan: Latency-Quality Tradeoffs and Task Offloading in Multi-node Next Generation RANs
复制标题
DOI：
10.23919/wons51326.2021.9415574
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Ayman Younis;Brian Qiu;D. Pompili
中科院分区：
文献类型：
--
作者：
Ayman Younis;Brian Qiu;D. Pompili
下一代无线电接入网络（NG-RAN）是在无线无线电接入点（RAP）的边缘处提供云计算和无线电能力的灵活分布的新兴范例。边缘计算为漫游的最终用户弥合了差距，使其能够访问丰富的服务和应用程序。在本文中，我们提出了一个多边缘节点的任务卸载系统，即，QLRan，一种用于NG-RAN中延迟和质量权衡任务分配的新型优化解决方案。考虑到服务延迟、质量损失和边缘容量的约束，联合任务卸载、延迟和结果质量损失（QLR）的问题被公式化以最小化用户设备（UE）任务卸载效用，其通过任务完成时间和QLR成本的减少的加权和来测量。QLRan优化问题被证明是一个混合非线性规划问题，这是一个NP-难问题。为了有效地解决QLRan优化问题，我们利用了基于线性规划（LP）的方法，该方法可以通过使用凸优化技术来解决。此外，可编程NG-RAN测试床，其中中央单元（CU），分布式单元（DU），和UE使用OpenAirInterface（OAI）软件平台虚拟化，以表征性能的数据输入，内存使用，和平均处理时间相对于QLR水平。仿真结果表明，该算法在不同的配置下都能显著改善网络延迟。
Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, and edge capacity, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are virtualized using the OpenAirInterface (OAI) software platform to characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different conflgurations.