Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.

中文翻译：

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LACO：超越 5G 网络中的延迟驱动网络切片编排

网络切片有望成为即将到来的 5G 网络及以后的游戏规则改变者，通过尚未开发的垂直行业利润扩大电信业务生态系统。这意味着鉴于大量预定义的要求，必须保证每个切片的异构服务级别协议 (SLA)。在本文中，我们开创了一种新颖的无线电切片编排解决方案，可在多租户环境中同时提供延迟和吞吐量保证。利用可靠的数学框架，我们通过基于多臂强盗 (MAB) 的协调器 LACO 来利用探索与开发范式，该协调器在没有流量需求或信道的先验知识的情况下做出自适应资源切片决策质量统计。与对底层系统视而不见的传统 MAB 方法相反，LACO 依靠系统结构信息来加速决策。在初步模拟活动凭经验证明了我们解决方案的有效性之后，我们使用现成的设备提供了 LACO 的稳健实现，以完全模拟现实的网络条件：当 LACO 到位时，在可负担的计算时间内测量接近最佳的结果。