Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner Online publication date: Tue, 21-Mar-2023
by Monika; Om Prakash Sangwan
International Journal of Grid and Utility Computing (IJGUC), Vol. 14, No. 1, 2023
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.
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