Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner. (17th March 2023) - ,; Sangwan, Om Prakash
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.