Cloud computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on-demand. Autoscaling is an important cloud computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling infrastructure to create greener computing enviornments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case-study showing the energy consumption/cost reduction produced by this model-driven approach.
Also, Dougherty, et al. REF shows an approach to optimizing configuration and cost of auto-scaling cloud infrastructure.
Model-driven Auto-scaling of Green Cloud Computing Infrastructure
Future Gener. Comput. Syst.
Future Gener. Comput. Syst.
Computer Science