Service recommendation in JointCloud environments: An efficient regret theory-based Qos-aware approach

With the proliferation of data-intensive applications, there arises an urgent demand for a substantial amount of cloud services to meet their requirements for data analysis. This globalized yet cooperative business landscape necessitates new cooperative models across the world. JointCloud, as a novel cross-cloud cooperation computing model, takes the first step towards establishing an evolving cloud ecosystem where all cloud service providers could collaboratively serve globalized computation needs. The collaboration among various cloud service providers enhances both the availability and Quality of Services(QoS) of cloud services, enabling a cloud service provider to concurrently serve users with differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service recommendations, rendering conventional approaches obsolete and inefficient. Thus, there is an urgent need to improve the efficiency and effectiveness of the service recommendation method, which is of vital importance for the JointCloud environment. In this paper, we present a two-stage efficient regret theory-based service recommendation method for the JointCloud environment. In the first stage of our proposed method, we cluster the cloud service providers to reduce the choice space to improve the efficiency of cloud service recommendations. In the second stage, we meticulously identify the most appropriate services within one cluster. To enhance the overall rationality of service recommendation, we introduce a subjective and objective combined weighting method and a regret theory based ranking method. Extensive experimental results demonstrate that our approach can facilitate fast and accurate service recommendations.