  Scalability is an important characteristic of cloud computing. With
scalability, cost is minimized by provisioning and releasing resources
according to demand. Most of current Infrastructure as a Service (IaaS)
providers deliver threshold-based auto-scaling techniques. However, setting up
thresholds with right values that minimize cost and achieve Service Level
Agreement is not an easy task, especially with variant and sudden workload
changes. This paper has proposed dynamic threshold based auto-scaling
algorithms that predict required resources using Long Short-Term Memory
Recurrent Neural Network and auto-scale virtual resources based on predicted
values. The proposed algorithms have been evaluated and compared with some of
existing algorithms. Experimental results show that the proposed algorithms
outperform other algorithms.
