Open AccessWeb services selection a perspective of computational physics
Author(s) -
Marco Adarme,
Miguel Jimeno,
Eduard Puerto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1587/1/012017
Subject(s) - web service , cloud computing , computer science , variety (cybernetics) , perspective (graphical) , software deployment , services computing , service (business) , convergence (economics) , quality of service , quality (philosophy) , service oriented architecture , ws policy , selection (genetic algorithm) , world wide web , data science , software engineering , web development , artificial intelligence , web application security , philosophy , economy , epistemology , economics , economic growth , operating system , computer network
Concerning computational physics, web services are conceived as mathematical units that are experienced in different systems that offer service composition. Due to the exponential growth of web services and their deployment on cloud platforms, quality of service parameters have now become an essential factor when searching for and selecting services that must satisfy specific non-functional requirements of a user application. A variety of service components are highly configurable and are dynamic scenarios because a significant number of services can meet these requirements. This work analyzes the systemic perspective of approaches for the selecting and searching of web services that have specifications of optimization strategies based on the configurable quality of service parameters with test scenarios in cloud environments that have a considerable number of services as input. The study shows that policies based on artificial intelligence and related areas are the ones with the most significant convergence, and the approaches analyzed to give a perspective of future work aimed at strategies based on automatic learning.