Online Dynamic Container Rescheduling for Improved Application Service Time

Abstract

Despite their maturity and robustness, container orchestration platforms still suffer from some limitations. One of those concerns the lack of runtime adaptability of the scheduler to the overall cluster status as (i) it instantiates containers with local optimization in mind i.e. it only considers the container-specific predefined requirements which may lead to a sub-optimal overall cluster state and (ii) it does not reshuffle the deployed containers at runtime based on container observed behavior and interdependencies. These limitations become even more apparent within volatile load contexts. This work proposes an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. To this extend, two distinct combinatorial optimization heuristics, Simulated Annealing and Particle Swarm Optimization, are evaluated and compared on their respective effectiveness and efficiency as well as on their relative performance towards the optimal solution obtained by Integer Linear Programming. Additionally, the impact of the proposed system on application service time is validated by means of two complementary use-cases, an event-based IoT data-hub platform and a web-based e-commerce app, with an average improvement of the end-to-end service time of 21.6% and 13.1% respectively.