A review of microservices autoscaling with formal verification perspective

• Published in: Software - Practice and Experience
• Main Author: Jawaddi S.N.A.; Johari M.H.; Ismail A.
• Format: Article
• Language: English
• Published: John Wiley and Sons Ltd 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136984240&doi=10.1002%2fspe.3135&partnerID=40&md5=8f6e93508ba189b1ca0b43f0ca82724e

The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA. Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. Hence, it is significant to gather and summarize these approaches to foster future innovation. Meanwhile, a few reviews have been published concerning microservices from different aspects. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. This study highlights the recent contributions in three inter-related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Due to limited resources on verification for microservice autoscaling, we widen the perspective by considering the verification for autoscaling in cloud-based systems. Based on our findings, we found that the formal method is not a new thing in verifying the autoscaling policies in cloud-based systems, and one recent study that implements the formal method in the microservices area has been identified. Apart from the autoscaling techniques, we have also determined several factors that have been a concern in scaling the microservices as well as the relatable metrics. Meanwhile, from a verification perspective, we identified that probabilistic model checking is the common formal verification technique used to verify microservices and cloud autoscaling. Finally, we recommend open challenges from two perspectives which highlight the verification for existing microservice autoscaling and verification for ML-based microservice autoscaling. © 2022 John Wiley & Sons Ltd.