Abstract-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 thresholdbased 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.
This work is continued in REF , where two Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are used.
Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network
International Journal of Advanced Computer Science and Applications (IJACSA), 7(12), 2016
Computer Science