Optimal Scaling Configurations for Microservice-Oriented Architectures Using Genetic Algorithms

Genetic algorithms (GAs) are a powerful tool for solving multi-objective optimization problems. Resource allocation and scaling of cloud systems typically involve multiple conflicting objectives, such as high through putin the presence of failures, cost, and reduced latency. Microservice-based architectures introduce additional complexities since the underlying services respond differently to different workloads. In this work, the performance of two multi-objective GAs is compared on the problem of finding efficient scaling configurations of a microservice-based architecture. Results show that while the use of GAs is effective at finding efficient configurations, GAs can not be used for larger systems involving many microservices or for systems that make use of caching.