Hybrid Grouping Genetic Algorithm for Large-Scale Two-Level Resource Allocation of Containers in the Cloud

Cloud container resource allocation aims to find container placements in cloud Virtual Machines (VM) and Physical Machines (PM) such that overall energy consumption is minimised A resource allocation architecture where application containers are consolidated into cloud VMs in a container-VM-PM model is common practise in data centers. The VM layer may provide additional administrative or security features, but adds complexity to the optimization problem when deploying containers initially on a large scale. Research addressing this two-level resource allocation is limited, some of the recent work try to optimise consolidation of containers to VM layer separately from consolidation of VMs to PMs, which results in large portions of the search space remaining unexplored. A Grouping Genetic Algorithm (GGA) framework that can simultaneously optimize consolidation on both levels is promising. However, for large instances of the two-level optimisation, it may suffer from premature convergence and limited population diversity. In this work, we propose a new fixed-length crossover operator that is designed to improve population diversity and exploration in GGA for container resource allocation optimisation. We also propose problem-specific Best-Fit and Largest VM heuristic operators to aid local search by rearranging containers from the lower fitness PMs at the chromosome tail into existing VMs and PMs with better utilization when possible. We demonstrate that with the newly developed operators, the proposed GGA can significantly reduce energy consumption in large-scale test cases.