Surrogate-Assisted Online Optimisation of Cloud IaaS Configurations

Elasticity refers to the auto-scaling ability of clouds towards optimally matching their resources to actual demand conditions. An important problem facing the infrastructure and service providers is how to optimise their resource configurations online, to elastically serve time-varying demands. Most scaling methodologies provide resource reconfiguration decisions to maintain quality properties under environment changes. However, issues related to the timeliness of such reconfiguration decisions are often neglected. In this paper, we present a methodology for online optimisation of cloud configurations. We first employ a search-based approach to extract near-optimal configurations considering conflicting performance and business quality attributes. Towards reducing the burden of time-consuming evaluations of configurations´ quality, we develop surrogate models to predict their quality based on history observations. Finally, we evaluate our technique using Cloud Sim-based cloud simulation. Our experimental results show that the use of surrogates can produce high quality configurations with lead time of seconds and prediction error within 6%.