Multi-Objective Optimisation of Container Orchestration Systems

The ever-increasing global demand for optimized resource utilization, energy efficiency, and rapid response times in cloud computing environments necessitates innovative approaches for resource management. Traditional cloud systems predominantly cater to large-scale infrastructures, often neglecting smaller-scale environments, such as edge computing infrastructures. In response to this gap, this paper introduces a novel Multi-Objective Stochastic Gradient Descent (MOSGD) approach designed to enhance the efficiency of the application placement processes beyond the cloud and closer to the edge of the network. The MOSGD optimisation addresses two crucial objectives: energy consumption and execution time. We meticulously modelled and integrated the two conflicting objectives into a unified cost function to minimise resource consumption and response time. To validate the MOSGD approach, we deployed a real-life environment utilizing the Carinthian Computing Continuum infrastructure as the target platform. The results of this research exhibit significant performance enhancements compared to two conventional methods. The findings indicate an improvement of up to 80% in energy efficiency and up to 30% reduction in execution time. These outcomes underscore the potential of the MOSGD approach to outperform traditional techniques in scenarios where large-scale procedures may exhibit suboptimal performance.