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:为了容纳低延迟和计算密集型服务,如物联网(IoT),5G 网络预计具有云和边缘计算能力。为此,我们考虑一个通用的网络设置,其中执行分析相关任务的设备可以部分处理任务并将其剩余部分卸载到基站,然后将其重新路由到云和/或边缘服务器。为了考虑到潜在的不可预测的流量需求和边缘网络动态性,我们把资源分配问题构建为一个在线凸优化问题,具有服务违规约束,并且允许邻近节点之间的有限通信。为了解决这个问题,我们提出了一种在线分布式(跨节点)原始-对偶算法,并证明它实现了次线性的后悔和违规率;实际上,所实现的较好的已知集中式替代方案的边界与其相同。我们的结果进一步得到了公开的米兰数据集的支持。