A Lightweight Workload-Aware Microservices Autoscaling with QoS Assurance.

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, ecient resource management for microservices poses a signicant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineective. 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 EcientMicroserviceAutoscaling), a lightweight microservice resource manager that nds ecient 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 nd close to optimum resource allocation and save up to 33% resource compared to the commercial rule-based resource allocations.