Metrics-Based Auto Scaling Module for Amazon Web Services Cloud Platform
One of the key benefits of moving an application to the cloud is the ability to easy scale horizontally when the workload increases. Many cloud providers offer a mechanism of auto scaling which dynamically adjusts the number of virtual server instances, on which given system is running, according to some basic resource-based metrics like CPU utilization. In this work, we propose a model of auto scaling which is based on timing statistics: a high order quantile and a mean value, which are calculated from custom metrics, like execution time of a user request, gathered on application level. Inputs to the model are user defined values of those custom metrics. We developed software module that controls a number of virtual server instances according to both auto scaling models and conducted experiments that show our model based on custom metrics can perform better, while it uses less instances and still maintains assumed time constraints.
KeywordsCloud computing Scalability Auto scaling Custom metrics Load balancing
This work was supported by NCBiR of Poland (No. INNOTECH-K3/IN3/46/229379/NCBR/14).
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