Small cells with edge computing are densely deployed in 5G mobile networks to provide high throughput communication and low-latency computation. The flexibility of edge computation is empowered by the deployment of lightweight container-based microservices. In this paper, we take the first step toward optimizing the microservice management in small-cell networks. The prominent feature is that each microservice consists of multiple image layers and different microservices may share some basic layers, thus bringing deep coupling in their placement and service provision. Our objective is to minimize the expected total latency of microservice requests under the storage, communication and computing constraints of the sparsely interconnected small cell nodes. We formulate a binary quadratic program (BQP) with the multi-dimensional strategy of the image layer placement, the access selection and the task assignment. The BQP problem is then transformed into an ILP problem, and is solved by use of a novel sphere-box alternating direction multipliers method (ADMM) with reasonable complexity $O(q^{4})$, where $q$ is the number of variables in the transformed problem. Trace-driven experiments show that the gap between our proposed algorithm and the optimal is reduced by 35$\%$ compared with benchmark algorithms.

翻译：在5G移动网络中，具备边缘计算能力的小基站被密集部署，以提供高吞吐量通信和低延迟计算。轻量级容器化微服务的部署增强了边缘计算的灵活性。本文首次迈出优化小基站网络微服务管理的步伐。其显著特征在于每个微服务由多个镜像层构成，不同微服务可能共享某些基础层，从而在部署与服务提供中产生深度耦合。我们的目标是在稀疏互联小基站节点的存储、通信和计算约束下，最小化微服务请求的预期总延迟。我们通过镜像层部署、接入选择和任务分配的多维策略构建了二元二次规划（BQP）问题。随后将BQP问题转化为整数线性规划（ILP）问题，并采用新颖的球盒交替方向乘子法（ADMM）求解，其计算复杂度为$O(q^{4})$，其中$q$为转化后问题的变量数。基于真实数据流的实验表明，与基准算法相比，本算法与最优解的差距降低了35$\%$。