A deep learning based framework for optimizing cloud consumer QoS-based service composition

Abstract

The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Building an accurate economic model for service composition has great attention to interest and importance for the Cloud consumer. A deep learning based service composition (DLSC) framework has been proposed in this paper. The proposed DLSC framework is considered an amalgamation between the deep learning long short term memory (LSTM) network and particle swarm optimization (PSO) algorithm. The LSTM network is applied to accurately predict the Cloud QoS provisioned values, and the output of LSTM network is fed to PSO algorithm to compose the best Cloud providers to contract with them for composing the needed services to minimize the consumer cost function. The proposed DLSC framework has been implemented using a real QoS dataset. According to the comparative results, it is found that the performance of the proposed framework outperforms the existing models with respect to the predictive accuracy and composition accuracy.