Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

An Energy Efficient Service Composition Mechanism Using a Hybrid Meta-heuristic Algorithm in a Mobile Cloud Environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms

Composition de service web dans le cloud computing

Les travaux de recherche menés autour de la composions de service web dans le cloud computing jusqu’à maintenant, représente une tentative pour résoudre le problème et il n’y pas aucune solution qui est considéré totalement optimale. La difficulté réside dans deux point : Tout d'abord, l'anticipation de tous les services nécessaires peut être un problème très difficile, surtout en cas de service de logiciel, car ils sont connus pour être des services simples et atomiques, publiés par différents éditeurs. Le deuxième défi est la sélection de l'optimal global service composé peut être considéré comme un problème d'optimisation NP-difficile. Dans cette thèse, nous concentrons sur la composition des services automatiques dans l'environnement multi clouds, par conséquent, nous utilisons l'algorithme Intelligent Water Drops, et une technique de programmation linéaire pour décider quelles bases de cloud à sélectionner pour la création du service composé