Impact of CPU utilization thresholds and scaling size on autoscaling cloud resources

Cloud computing is currently one of the most hyped information technology fields and it has become one of the fastest growing segments of IT. A cloud introduces a resource-rich computing model with features such as flexibility, pay per use, elasticity, scalability, and others. In the context of cloud computing, auto scaling and elasticity are methods used to assure SLO (Service Level Objectives) for cloud services as well as the efficient usage of resources. There are many factors related to the auto scaling mechanism that might affect the performance of the cloud services. One of such important factors is the setting of CPU thresholds that control the triggering of the auto scaling policies, for the purpose of adding or terminating resources from the auto-scaling group. Another important factor is the scaling size, which is the number of instances that will be added every time such provisioning process takes place to add more resources to cope with workload spikes. In this paper, we simulate and study the impact of setting the upper CPU utilization threshold and the scaling size factors on the performance of the cloud services. Another contribution of this paper is on formulating and solving optimization problems for tuning these parameters based on input loads, considering both the cost and SLO response time. The study helps in deciding about the optimal setting that enables the use of the least number of cloud resources to satisfy QoS or SLO requirements.