Improvements of the Reactive Auto Scaling Method for Cloud Platform

Abstract

Elements of cloud infrastructure like load balancers, instances of virtual server (service nodes), storage services are used in an architecture of modern cloud-enabled systems. Auto scaling is a mechanism which allows to on-line adapt efficiency of a system to current load. It is done by increasing or decreasing number of running instances. Auto scaling model uses a statistics based on a standard metrics like CPU Utilization or a custom metrics like execution time of selected business service. By horizontal scaling, the model should satisfy Quality of Service requirements (QoS). QoS requirements are determined by criteria based on statistics defined on metrics. The auto scaling model should minimize the cost (mainly measured by the number of used instances) subject to an assumed QoS requirements. There are many reactive (on current load) and predictive (future load) approaches to the model of auto scaling. In this paper we propose some extensions to the concrete reactive auto scaling model to improve sensitivity to load changes. We introduce the extension which varying threshold of CPU Utilization in scaling-out policy. We extend the model by introducing randomized method in scaling-in policy.