Layer-aware Collaborative Microservice Deployment toward Maximal Edge Throughput

Lightweight container-based microservice has been widely advocated to promote the elasticity of edge cloud. The inherent layered structure of containers offers a compelling way to cope with the resource scarcity of edge servers through layer sharing, which can significantly increase storage utilization and improve the edge throughput. Recent studies show that it is possible to share layers not only within the same server but also between servers, which microservice deployment can take full advantage of. In this paper, we investigate the problem of how to collaboratively deploy microservices by incorporating both intra-server and inter-server layer sharing to maximize the edge throughput. We formulate this problem into an integer linear programming form and prove it as NP-hard. We propose a randomized rounding based heuristic algorithm, and conduct formal analysis on the guaranteed approximation ratio. Through extensive experiments, we verify the efficiency of our proposed algorithm, and the results demonstrate that it can deploy 6× and 12× more microservice instances, and improve the edge throughput by 27.74% and 38.46% in comparison with state-of-the-art strategies.

I. Introduction

Edge cloud has emerged as a promising platform complementary to cloud systems by provisioning computation resources at the network edge. Owing to the advantage on end user proximity, edge cloud is ideal for many delay-sensitive applications such as self-driving, healthcare, augmented reality, and smart manufacturing. Meanwhile, in comparison with central cloud empowered by large datacenters, edge cloud resources are relatively scarce. Therefore, how to mostly effectively utilize the edge resources has become one of the main research focuses recently. On the other hand, container-based microservices have been advocated as an edge service provision paradigm, due to its lightweight advantages, which offer flexibility and elasticity over conventional virtual machines (VMs) [1]–[3].