Optimizing Layerwise Microservice Management in Heterogeneous Wireless Networks

TL;DR

This paper proposes an optimization framework for managing layered microservices in 5G small-cell networks, aiming to reduce latency by efficiently placing shared image layers under network constraints.

Contribution

It introduces a novel BQP formulation for layered microservice placement and a sphere-box ADMM solution with reduced complexity, improving latency performance.

Findings

Reduced latency gap by 35% compared to benchmarks

Effective handling of shared image layers in microservice placement

Novel ADMM algorithm with $O(q^{4})$ complexity

Abstract

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.