No abstract

Recently, more and more traditional services are being migrated into a cloud computing environment that makes the quality of service (QoS) becomes an important factor for service selection and optimal service composition while forming cross-cloud service applications. Considering the nonlinear and dynamic property of QoS data, it is so difficult to achieve dynamic prediction while designing a QoS prediction method with unsatisfactory prediction accuracy. It is thus desirable to explore how to design an effective approach by incorporating some intelligent techniques into the QoS prediction method to improve prediction performance. In this paper, motivated by the adaptive critic design and Q-learning technique, we propose a novel QoS prediction approach to serve this purpose through the combination of fuzzy neural networks and adaptive dynamic programming (ADP), i.e., an online learning scheme. This approach extracts fuzzy rules from QoS data and employs the ADP method to parameter learning of the fuzzy rules. Moreover, we provide a convergence boundedness result for our proposed approach to guarantee the stability. Experimental results on a large-scale QoS service data set verify the prediction accuracy of our proposed approach.INDEX TERMS Quality of service (QoS), QoS prediction, fuzzy neural network, adaptive dynamic programming, cloud services.

Extreme learning machine (ELM) as a learning algorithm for neural networks (NN) could provide the best generalization performance at extremely fast leaning speed. Through the use of ELM, it is thus possible to improve the existing schemes especially the ones whose learning speed is not fast enough while addressing control problems. As a popular NN-based approach for control applications, direct heuristic dynamic programming (DHDP) with a good capability of adaptive learning has been successfully applied to solve control problems. But limited by slow learning algorithms in NN, it imposes very challenging obstacles to the real-time controller design of DHDP, which keeps it from widely applied. In this paper, driven by the interest of improving learning speed of DHDP while maintaining its good approximation performance, we employ ELM as a learning algorithm in DHDP. The proposed ELM-based DHDP learning scheme is tested on a cart-pole balancing control problem. The simulation results show the proposed scheme has better learning performance than traditional DHDP. Furthermore, this paper provides a novel idea of applying ELM in control problems.