Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

<span>The expanding propensity of organization users to utilize cloud services urges deliver in a service pool with variety functional and non-functional attributes from online providers. brokers must intense rivalry competing one another provide quality (QoS) enhancements. Such prompts troublesome muddled providing composite on the using simple selection composition approach. Therefore, is considered non-deterministic polynomial (NP-hard) economically motivated problem. Hence, developing reliable economic model for tremendous interest have importance consumer. This paper provides “A location-aware deep learning framework improving QoS-based consumers”. The proposed firstly reducing dimensions data. Secondly, it applies combination long short-term memory network particle swarm optimization algorithm additionally considering location parameter correctly forecast QoS provisioned values. Finally, composes ideal need reduce customer cost function. suggested framework's performance has been demonstrated real dataset, proving that superior current models terms prediction accuracy.</span>