Quality‐aware service integration in cloud computing information infrastructures typically refers to selecting a suitable subset of services out of those available in order to fulfill a user’s request, considering multiple quality of service (QoS) metrics like response time, cost, availability, and reliability constraints. Since this optimization problem is NP‐complete and possesses a large search space, it remains an active topic of cloud computing research. Most metaheuristic techniques developed to solve this problem work under assumptions of relatively static‐QoS conditions and require continuous service monitoring in order to function. However, modern cloud environments often exhibit highly dynamic workloads and changing resource availabilities, leading to varying QoS characteristics. This work proposes a hybrid deep learning and metaheuristic‐based framework for QoS‐aware cloud service integration. It synergizes a deep learning QoS prediction model and the elephant herding optimization (EHO) algorithm for search and delivers near‐optimal service integration decisions. EHO was chosen over the most recent/metahybrid evolutionary algorithms due to its empirical efficiency and effectiveness in tackling the large‐scale QoS‐aware service integration problem. The proposed method consistently outperforms existing baselines like genetic algorithm (GA), particle swarm optimization (PSO), and stand‐alone EHO in terms of QoS metrics measured on a simulated cloud platform with publicly available service datasets. The average response time of the selected services under the proposed method is between 20% and 45% lower, service availability is always above 99.5%, the service cost can be up to 40% lower, and reliability can be increased by about 2%–4%. In addition to these performance gains, the benefits of the proposed framework include decoupling service integration decisions from real‐time monitoring of cloud resources, due to QoS prediction. Specifically, a deep learning–based forecasting model is employed to quickly adapt to dynamic/cloud workloads and produce reliable predictions of QoS values even when the underlying cloud infrastructure is changing rapidly. This allows for applying the proposed framework to challenging large‐scale and complicated environments like IoT infrastructures, smart cities, and industrial cloud platforms, which need to take scalability, heterogeneity of service compositions, and limited resource capabilities of edge devices into account. This predictive model also offers a valuable tool for designing future cloud orchestration systems, integrating seamlessly with current real‐time cloud management platforms. The limitation of this work is the dependency on acquiring enough historical data to train the deep learning model. One possible way to address this is through transfer learning or data augmentation techniques.
Hong et al. (Thu,) studied this question.