Penerapan Microservices dan Amazon Elastic Container Service untuk Mendukung Scalability - Antonius Tanuwijaya; Henry Novianus Palit; Agustinus Noertjahyana (Jurnal Infra, 2021)
The technology age under development provides the impact of increasing the number of users in a system may also increase the workload received by the server. This condition is experienced in PT. X, where the server cannot handle the growing workload over time, this makes the server overloaded and slow in response until it gets to the server condition is down and unreachable by the user. This research tried to provide solutions to the problems faced by PT. X by applying a system of microservices and Amazon Elastic Container Service. By applying microservices then all services will be split into independent and can ease the workload of the server. Moreover, with the combination of Amazon ECS then the process of scaling will be more effective only on the service that is experiencing an overload condition so that the process of scaling can adjust the conditions of the workload on the server at that time. The scaling process will allow the system to increase or decrease the number of tasks performed without a lack or excessive use of resources. Based on analysis of the implementation of microservices and the Amazon ECS on the PT. X system, It can be concluded that the scalable microservices system produces a lower average response time with a difference of 805.56% compared to unscalable microservices and 38% compared to monolithic, then the resulting deviation is 902.22% lower than unscalable microservices and 216.87% lower than monolithic, then the resulting throughput is higher by 22018.61 requests/minutes from unscalable microservices and 24524.16 requests/minutes from monolithic. For a maximum concurrent user comparison between a scalable microservices system, an unscalable microservices, and monolithic of 2000:1454:28. In addition, the CPU usage of scalable microservices systems is 20%-21% lower, especially at login, generate access tokens, and get schedules when compared to unscalable microservices systems, due to workload sharing system with replication tasks. Additionally, the use of resources can adjust to workload conditions dynamically and efficiently