论文标题

如何共享:在工业微服务部署中平衡层和连锁共享

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

论文作者

Liu, Yuxiang, Yang, Bo, Wu, Yu, Chen, Cailian, Guan, Xinping

论文摘要

随着智能制造的快速开发,以边缘计算为导向的微服务平台正在成为生产控制的重要组成部分。在微服务的集装箱部署中,层共享可以减少由图像拉的图像引起的巨大带宽消耗,而链条共享可以减少由微服务之间的通信引起的通信开销。这两种共享方法使用每个微服务的特征在部署过程中共享资源。但是,由于边缘服务器的资源有限,很难同时满足两种方法的优化目标。因此,通过平衡两种共享方法来实现服务响应效率的提高至关重要。本文研究了最佳的微服务部署策略,可以平衡微服务的层共享和连锁共享。我们建立一个问题,可以将微服务图像拉延迟和通信开销最小化,并通过模型重建将问题转换为线性约束的整数二次编程问题。通过连续的凸近似(SCA)方法获得部署策略。实验结果表明,提出的部署策略可以平衡两种资源共享方法。当同样考虑两种共享方法时,平均图像拉力延迟可以减少到基线的65%,并且平均通信开销可以减少到基线的30%。

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.

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