Prediction mechanisms for monitoring state of cloud resources using Markov chain model

Abstract

Cloud computing allows for sharing computing resources, such as CPU, application platforms, and services. Monitoring these resources would benefit from an accurate prediction model that significantly reduces the network overhead caused by unnecessary push and pull messages. However, accurate prediction of the computing resources is considered hard due to the dynamic nature of cloud computing. In this paper, two monitoring mechanisms have been developed: the first is based on a Continuous Time Markov Chain (CTMC) model and the second is based on a Discrete Time Markov Chain (DTMC) model. It is found that The CTMC-based mechanism outperformed the DTMC-based mechanism. Also, the CTMC-based mechanism outperformed the Grid Resource Information Retrieval (GRIR) mechanism, which does not employ prediction, and a prediction-based mechanism, which uses Markov Chains to predict the time interval of monitoring mobile grid resources, in monitoring cloud resources.