A Multiobjective Metaheuristic-Based Container Consolidation Model for Cloud Application Performance Improvement

This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service-time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service-time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler.