Autoscaling involves complex cloud operations that automate the provisioning and de-provisioning of cloud resources to support continuous development of customer services. Autoscaling depends on a number of decisions derived by aggregating metrics at the infrastructure and the platform level. In this paper, we review existing autoscaling techniques deployed in leading cloud providers. We identify core features and entities of the autoscaling operations as variables. We model these variables that quantify the interactions between these entities and incorporate workload time series data to calibrate the model. Hence the model allows proactive analysis of workload patterns and estimation of the responsiveness of the autoscaling operations. We demonstrate the use of this model with Google cluster trace data.
['Mehran Khan', 'Yan Liu', 'Hanieh Alipour', 'Samneet Singh']
Modeling the Autoscaling Operations in Cloud with Time Series Data