Cloud computing can lessen electricity intake through the usage of virtualized computational sources to provision an application's computational sources on demand. Auto-scaling is an essential cloud computing method that dynamically allocates computational sources to programs to healthy their modern hundreds precisely, thereby eliminating sources that could in any other case stay idle and waste electricity. This paper affords a model-pushed engineering method to optimizing the configuration, strength intake, and running fee of cloud auto-scaling infrastructure to create greener computing environments that lessen emissions on account of superfluous idle sources. The paper presents 4 contributions to the take a look at of model-pushed configuration of cloud auto-scaling infrastructure through explaining how digital system configurations may be captured in function fashions, describing how those fashions may be converted into constraint pleasure problems (CSPs) for configuration and strength intake optimization, displaying how greatest auto-scaling configurations may be derived from those CSPs with a constraint solver, and providing a case take a look at displaying the strength intake/fee discount produced through this model-pushed method.