To accommodate low latency and computation-intensive services, such as the Internet-of-Things (IoT), 5G networks are expected to have cloud and edge computing capabilities. To this end, we consider a generic network setup where devices, performing analytics-related tasks, can partially process a task and offload its remainder to base stations, which can then reroute it to cloud and/or to edge servers. To account for the potentially unpredictable traffic demands and edge network dynamics, we formulate the resource allocation as an online convex optimization problem with service violation constraints and allow limited communication between neighboring nodes. To address the problem, we propose an online distributed (across the nodes) primal-dual algorithm and prove that it achieves sublinear regret and violation; in fact, the achieved bound is of the same order as the best known centralized alternative. Our results are further supported using the publicly available Milano dataset.

翻译：为适应物联网（IoT）等低时延与计算密集型服务需求，5G网络需具备云与边缘计算能力。为此，我们考虑一种通用网络架构，其中执行分析相关任务的设备可部分处理任务，并将剩余部分卸载至基站，基站再将其路由至云端和/或边缘服务器。为应对潜在不可预测的流量需求与边缘网络动态性，我们将资源分配建模为具有服务违反约束的在线凸优化问题，并允许相邻节点间进行有限通信。针对该问题，我们提出一种在线分布式（跨节点）原始-对偶算法，并证明其可实现次线性遗憾与违反量；实际上，该算法达到的界与已知最优集中式替代方案同阶。我们进一步利用公开的Milano数据集验证了上述结论。