## 7 Related Work  
There is a limited amount of work focusing on the comprehensive approach to help practitioners to identify and choose features they need during the designing phase. A few studies on MSA [37; 38; 39; 40; 41; 2] present an overview of academic and grey literature. [PERSON] et al. [37] discover practical motivations behind using MSA and different types of MSA. In addition, existing research issues and potential future research items have been identified in this study. [PERSON] et al. [2] explore the potential challenges and obstacles. Furthermore, the advantages of MSA are explained by reviewing more than 50 studies. [PERSON] et al. [38] identify the quality attributes and architectural diagrams in MSA, as well as architectural challenges from 33 studies. [PERSON] et al. [40] focus on the practical motivations in MSA-related studies and provide overview of emerging standards and tools in MSA development. [PERSON] et al. [39] present publication trends and industrial adoptions of MSA with the help of more than 100 studies. [PERSON] et al. [41] study more than 50 studies and extract some useful information, such as current issues, opportunities, and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **AWS** & **Google Cloud** & **Microsoft Azure** \\ \hline Deployment/VM & AWS EC2 & Google Compute Engine & Azure VM \\ \hline Auto-scaling & AWS EC2 Auto-scaling & Google Computer Engine Auto-scaling & Azure Virtual Machine Scale Set \\ \hline Load Balancing/Server-side & AWS ELB & Google Cloud Engine Load Balancing & Azure Load Balancing \\ \hline Service Discovery/Server-side & AWS Route 53 & Google Cloud DNS & Azure DNS \\  & AWS Cloud Map & Google Cloud Engine Load Balancing & Azure Load Balancing \\ \hline Service Mesh and Sidecar Pattern & AWS AppMesh & Google Anthos Service Mesh & Azure Service Fabric \\ \hline Log Analysis & AWS Elasticsearch Service & Google Elasticsearch Service & Azure Elasticsearch Service \\  & AWS Redshift AWS Quicksight & Google BigQuery & Azure Data Lake Analytics \\  & AWS Athena & Google Cloud Debugger & Azure Application Insights \\ \hline Observability & Log Aggregation & AWS CloudWatch & Google Cloud Logging & Azure Application Insights \\ \hline Audit Logging & AWS Cloud Trail AWS Config & Google Audit Logs Inventory & Azure Monitor \\ \hline Distributed Tracing & AWS X-Ray & Google Cloud Debugger Google CloudTrace & Azure Monitor \\ \hline Monitoring & AWS CloudWatch Monitoring & Google Cloud Monitoring & Azure Monitor \\ \hline Provisioning and Configuration Management & AWS CloudFormation & Google Cloud Debployment Manager & Azure VM extensions Azure Automation \\ \hline Security & AWS Cognito & Google Firebase Authentication & Azure Active Directory B2C \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._potential future research items. Moreover, methods and techniques in MSA are examined. Finally, [PERSON] et al. [42] aim to identify quality attributes of MSA by focusing from a QA point of view.  
Some studies do not give a complete overview of MSA. They just focus on one aspect or feature of MSA. By the way of illustration, [PERSON] et al. [43] and [PERSON] et al. [44] focus on the microservice security, [PERSON] et al. [45] address the deployment and communication patterns in MSA, whereas [PERSON] et al. conduct a systematic literature review to reveal the approaches to be used to define MSA granularity and identify the metric to be used to evaluate MSA granularity.  
According to our observations, there is only one study to classify some technologies and patterns. [PERSON] et al. [4] present MSA evolution from scratch. Progression within MSA evolution is also detailed with the technology used in this period. In addition, tools and technologies in MSA are also provided regarding the predetermined categories.  
Our approach differs from studies in this section by addressing many aspects and also making characterization with a well-defined comprehensive characterization framework.