An HMM-based Method for Adapting Service-based Applications to Users’ Quality Preferences

Service-based application (sba) is composed of software services, and these services may be owned by the developing organization or third parties. to provide functionalities based on the user’s preferences, sba’s constitute services should be selected dynamically at runtime. for each distinct user’s request, we aim at finding a sequence of services which mostly satisfies user’s preferences. furthermore, we aim at reporting in a systematic manner the list of relevant contributions similar to our work focusing on the adaptation mechanisms. we applied hidden markov model (hmm) to propose a qos-based service selection method. the method is presented in three steps: modelling, learning, and qos-based selection. we used real-world qos dataset to investigate the fitness and the execution time of the method. we compared this work with gsa-based and pso-based service selection methods. we built and trained an hmm for selecting services for a given sequence of tasks in which the selected services are mostly aligned with user’s preferences. experimental results showed that our method achieves the maximum fitness in a reasonable time. since service-oriented environments are ever changing, unsupervised learning approaches like maximum likelihood estimation or viterbi training should be used to modify the elements and the probabilities of hmm.