Title: Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner

Authors: Monika; Om Prakash Sangwan

Addresses: Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India ' Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

Abstract: With the continuous proliferation of cloud services, the recommendation of optimal cloud service according to user requirements has become a critical issue and makes it highly infeasible for a single user to find a specific application with QoS requirements and thus, depends on other users' collected information about various cloud services. These collected QoS values are highly non-linear, complex, and uncertain. To deal with the given scenario, it is required to develop a recommender system for the prediction of unknown QoS values using some optimisation techniques. Therefore, we have employed a novel backpropagation-based ADP parameter tuning strategy with two basic prediction techniques for developing the self-adaptive intelligent system to provide an automatic parameter tuning capability to these techniques. To evaluate the proposed approach, we have done a simulation of the approach on a real QoS data set and experimental results show better prediction accuracy compared with other traditional approaches.

Keywords: cloud computing; QoS prediction; ADP parameter tuner; fuzzy C-means clustering; matrix factorisation; back propagation neural network.

DOI: 10.1504/IJGUC.2023.129699

International Journal of Grid and Utility Computing, 2023 Vol.14 No.1, pp.1 - 14

Received: 25 Jun 2020
Accepted: 29 Oct 2020
Published online: 21 Mar 2023 *