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Recently, web service composition technology has become familiar, and it raises the quality offered by the systems designed followed by the service-oriented architecture (SOA) framework. Web service composition mainly functions in dynamic environment, susceptible to the incidence of unpredictable disruption and modifications which could influence the performance of the system. Therefore, the ability to self-heal and manage the execution of web service composition can enhance the reliability and fault tolerance of the system. This study develops an optimal deep learning-based self-healing mechanism with failure prediction (ODL-SHMFP) model for web services. The proposed ODL-SHMFP technique aims to accomplish a self-healing model for minimizing the failure in web services.

Design of an Optimal Deep Learning-Based Self-Healing Mechanism With Failure Prediction Model for Web Services