A Genetic Programming Hyper-heuristic Approach for Online Resource Allocation in Container-Based Clouds

Abstract

The popularity of container-based clouds is its ability to deploy and run applications without launching an entire virtual machine (VM) for each application. Container-based clouds support flexible deployment of applications and therefore brings the potential to reduce the energy consumption of data centers. With the goal of energy reduction, it is more difficult to optimize the allocation of containers than traditional VM-based clouds because of the finer granularity of resources. Little research has been conducted for applying human-design heuristics on balanced and unbalanced resources. In this paper, we first compare three human-design heuristics and show they cannot handle balanced and unbalanced resources scenarios well. We propose a learning-based algorithm: genetic programming hyper-heuristic (GPHH) to automatically generate a suitable heuristic for allocating containers in an online fashion. The results show that the proposed GPHH managed to evolve better heuristics than the human-designed ones in terms of energy consumption in a range of cloud scenarios.