How to Share: Balancing Layer and Chain Sharing in Industrial Microservice Deployment

TL;DR

This paper proposes an optimal deployment strategy for microservices in edge computing that balances layer and chain sharing, significantly reducing image pull delay and communication overhead.

Contribution

It introduces a novel optimization model and solution method for balancing resource sharing in microservice deployment on edge servers.

Findings

Reduces image pull delay to 65% of baseline.

Reduces communication overhead to 30% of baseline.

Balances resource sharing methods effectively.

Abstract

With the rapid development of smart manufacturing, edge computing-oriented microservice platforms are emerging as an important part of production control. In the containerized deployment of microservices, layer sharing can reduce the huge bandwidth consumption caused by image pulling, and chain sharing can reduce communication overhead caused by communication between microservices. The two sharing methods use the characteristics of each microservice to share resources during deployment. However, due to the limited resources of edge servers, it is difficult to meet the optimization goals of the two methods at the same time. Therefore, it is of critical importance to realize the improvement of service response efficiency by balancing the two sharing methods. This paper studies the optimal microservice deployment strategy that can balance layer sharing and chain sharing of microservices. We build a problem that minimizes microservice image pull delay and communication overhead and transform the problem into a linearly constrained integer quadratic programming problem through model reconstruction. A deployment strategy is obtained through the successive convex approximation (SCA) method. Experimental results show that the proposed deployment strategy can balance the two resource sharing methods. When the two sharing methods are equally considered, the average image pull delay can be reduced to 65% of the baseline, and the average communication overhead can be reduced to 30% of the baseline.