Application of Artificial Neural Networks in Capacity Planning of Cloud Based IT Infrastructure

Abstract

Cloud is gaining popularity as means for saving cost of IT ownership and accelerating time to market due to ready-to-use, dynamically scalable computing infrastructure and software services offered on Cloud on pay-per-use basis. There is a an important change in the way these infrastructures are assembled, configured and managed. In this research we consider the problem of managing computing infrastructure which are acquired from Infrastructure as a service (IaaS) providers, which support the execution of web applications whose work load experience huge fluctuations over the time. The operating state of the web applications on the cloud is determined by the work load, service rate and utility gain of the web services, As these parameters are changing dynamically, we could not get the exact relationship between these parameters using conventional methods. We can use the Back propagation training algorithm of artificial neural networks to solve this problem. By training the Artificial neural network with the past data, we can estimate the future numbers. In this paper we proposed a artificial neural network based model that can be used for guiding the capacity planning activity. This paper reports on an investigation on the application of ANNs in Capacity planning of cloud based infrastructure. A multi-layer feed-forward artificial neural network (ANN) with error back-propagation learning is proposed for calculation of number of reserved instances for future use. Matlab Neural Network Toolbox is used for simulation of required ANN and considering Amazon web services as a IaaS provider.