Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner. (17th March 2023)

• Record Type: Journal Article
• Authors: Sangwan, Om Prakash
• Is Part Of: International journal of grid and utility computing. Volume 14:Number 1(2023)
• Page Start: 1
• Page End: 14
• Publication Date: 2023-03-17
• Subjects: cloud computing -- QoS prediction -- ADP parameter tuner -- fuzzy C-means clustering -- matrix factorisation -- back propagation neural network; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.