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.

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The popularity of serverless computing has been fueled by its operational simplicity, pay-per-use pricing model, and the ability to autoscale. However, there is a lack of comprehensive reviews that focus on the autoscaling context in serverless computing. In this paper, we address this gap by proposing a taxonomy of autoscaling properties for serverless computing. To gather relevant information, we review recent contributions on autoscaling in serverless computing from 2018 to 2022. Using the proposed taxonomy, we analyze the existing autoscaling solutions. Our analysis reveals that the scaling objectives explored by researchers are limited to certain elements, and the existing serverless autoscaling approaches do not provide guarantees that the scaling policies or strategies can meet the Service Level Agreement (SLA) requirements. We conclude by recommending open challenges from three perspectives: verification of autoscaling, energy-driven autoscaling, and anomaly-aware autoscaling. These challenges highlight the need for future research to address the limitations of existing autoscaling approaches and provide more robust and reliable autoscaling mechanisms for serverless computing.

Microservices can independently adjust their capacity to match demand while the autoscaling feature in cloud computing facilitates the users (i.e., developers) to provision resources required by their applications with less human intervention. Kubernetes is one of the well-known technologies used to deploy microservice-based applications and many autoscaling methods have been proposed to improve the behavior of its Horizontal Pod Autoscaler (HPA). Despite many research efforts have been recently devoted to investigate microservice autoscaling, there is still a lack of studies that consider the correctness of the scaling decision as well as the effect of the scaling process on host energy consumption and system scalability factors. Therefore, in this work we aim to take into account formal verification in the microservice autoscaling decision-making process by utilizing Markov Decision Process (MDP) and probabilistic model checking. To this end, we propose five MDP model variations, inspired by the scaling behavior of Kubernetes-based HPA, analyze the performance of the models from a combination of metrics. The Base Model is built by considering the CPU utilization metric in decision making, while the other models extend it by including several additional metrics to enhance the decision (i.e., latency, response time, energy consumption, and performance change). We use the PRISM-games model checker for the analysis purpose by verifying the properties specified in Probabilistic Computation Tree Logic (PCTL). Through our experiments, the decision made by the Full Model which considers all the metrics has outperformed the other models in terms of minimizing the energy consumption and leading to a good scalability level (i.e. scalability near to 1).