Practical Efficient Microservice Autoscaling with QoS Assurance

TL;DR

This paper introduces PEMA, a lightweight and adaptive autoscaling method for microservices that reduces resource usage by up to 33% while maintaining QoS, addressing the limitations of existing ML-based approaches.

Contribution

PEMA is a novel, workload-aware autoscaling framework that efficiently manages microservice resources through opportunistic reduction, improving over existing methods.

Findings

PEMA reduces resource usage by up to 33%.

PEMA maintains QoS in microservice deployments.

PEMA adapts efficiently to changing workloads.

Abstract

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineffective. Meanwhile, machine learning (ML) based approaches that try to capture the complex relationships in microservices require extensive training data and cause intentional SLO violations. Moreover, these ML-heavy approaches are slow in adapting to dynamically changing microservice operating environments. In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA's lightweight design enables novel workload-aware and adaptive resource management. Using three prototype microservice implementations, we show that PEMA can find efficient resource allocation and save up to 33% resource compared to the commercial rule-based resource allocations.