Microservice Dynamic Architecture-Level Deployment Orchestration (Extended Version)

TL;DR

This paper presents a novel run-time global adaptation method for microservice architectures, using an integrated timed modeling language and an algorithm to optimize deployment orchestrations for maximum computational load.

Contribution

It introduces a timed extension of the ABS language and SmartDeployer tool for architecture-level reconfiguration, enabling more effective microservice deployment adaptation.

Findings

Effective simulation on a realistic microservice application.

Outperforms traditional local scaling techniques in avoiding slowdowns.

Reduces message loss and latency during reconfiguration.

Abstract

In the context of the BI-REX (Big Data Innovation and Research Excellence) competence center SEAWALL (SEAmless loW lAtency cLoud pLatforms) project (scientific coordinator Prof. Maurizio Gabbrielli) we develop a novel approach for run-time global adaptation of microservice applications, based on synthesis of architecture-level reconfiguration orchestrations. More precisely, we devise an algorithm for automatic reconfiguration that reaches a target system Maximum Computational Load by performing optimal deployment orchestrations. To conceive and simulate our approach, we introduce a novel integrated timed architectural modeling/execution language based on an extension of the actor-based object-oriented Abstract Behavioral Specification (ABS) language. In particular, we realize a timed extension of SmartDeployer, whose ABS code annotations make it possible to express architectural properties. Our Timed SmartDeployer tool fully integrates time features of ABS and architectural annotations by generating timed deployment orchestrations. We evaluate the applicability of our approach on a realistic microservice application taken from the literature: an Email Pipeline Processing System. We prove its effectiveness by simulating such an application and by comparing architecture-level reconfiguration with traditional local scaling techniques (which detect scaling needs and enact replications at the level of single microservices). Our comparison results show that our approach avoids cascading slowdowns and consequent increased message loss and latency, which affect traditional local scaling.