Failure prediction by relevance vector regression with improved quantum-inspired gravitational search

Abstract Modern data centers coordinate hundreds of thousands of heterogeneous tasks aiming at providing highly reliable cloud computing services. Failure prediction is of vital importance in the analysis of cloud reliability. Recently, a novel kernel learning method called relevance vector machine (RVM) has been widely applied to solve nonlinear predicting problems and has been verified to perform well in many situations. However, it remains a great challenge for existing approaches to acquire the optimal RVM parameters. In this research, an artificial immune system is introduced into a Quantum-inspired Binary Gravitational Search Algorithm (QBGSA) in order to improve the convergence rate of standard QBGSA. In addition, a hybrid model of RVM with improved QBGSA called IQBGSA-RVM is proposed that aims to predict the failure time of cloud services. To evaluate the effectiveness of IQBGSA-RVM in failure prediction, its predicting performance is compared with that of the following algorithms, all of which employs RVM: chaotic genetic algorithms, binary gravitational search algorithms, binary particle swarm optimization, quantum-inspired binary particle swarm optimization and standard QBGSA. The experimental results show that the IQBGSA-RVM model is either comparable to the other models or it outperforms them, to say the least.