An Auto-Scaling Approach for Microservices in Cloud Computing Environments

Recently, microservices have become a commonly-used architectural pattern for building cloud-native applications. Cloud computing provides flexibility for service providers, allowing them to remove or add resources depending on the workload of their web applications. If the resources allocated to the service are not aligned with its requirements, instances of failure or delayed response will increase, resulting in customer dissatisfaction. This problem has become a significant challenge in microservices-based applications, because thousands of microservices in the system may have complex interactions. Auto-scaling is a feature of cloud computing that enables resource scalability on demand, thus allowing service providers to deliver resources to their applications without human intervention under a dynamic workload to minimize resource cost and latency while maintaining the quality of service requirements. In this research, we aimed to establish a computational model for analyzing the workload of all microservices. To this end, the overall workload entering the system was considered, and the relationships and function calls between microservices were taken into account, because in a large-scale application with thousands of microservices, accurately monitoring all microservices and gathering precise performance metrics are usually difficult. Then, we developed a multi-criteria decision-making method to select the candidate microservices for scaling. We have tested the proposed approach with three datasets. The results of the conducted experiments show that the detection of input load toward microservices is performed with an average accuracy of about 99% which is a notable result. Furthermore, the proposed approach has demonstrated a substantial enhancement in resource utilization, achieving an average improvement of 40.74%, 20.28%, and 28.85% across three distinct datasets in comparison to existing methods. This is achieved by a notable reduction in the number of scaling operations, reducing the count by 54.40%, 55.52%, and 69.82%, respectively. Consequently, this optimization translates into a decrease in required resources, leading to cost reductions of 1.64%, 1.89%, and 1.67% respectively.