Dynamic semantic‐based green bio‐inspired approach for optimizing energy and cloud services qualities

Abstract

Currently, everybody can access and leverage existing services on the Cloud from a wide variety of mobile devices at any time and from anywhere (at home, at work, in the car, etc). The massive use of new heterogeneous mobile devices and technologies for discovering and deploying cloud services has led a trade‐off between costs and improved quality of services (eg, fast response time, low cost, improved security, the reduction of energy consumption, and considerable emissions of carbon). This trade‐off has led most cloud service providers to call for new intelligent, faster, and energy‐saving solutions. This paper aims to propose a new approach based on Semantic Web technologies and Ant Colony Optimization algorithm, which intends to reduce the energy consumption of a wide variety of cloud services. Our approach is generic and, therefore, offers to customers a flexible infrastructure where they can easily perform their preferences. The effectiveness and energy saving of our proposal have been validated and evaluated through multiple experiments on random and real‐world data sets.

HTG: A heterogeneous topology aware model to improve cold start in cloud service QoS prediction

“…Zhu et al 35 propose an adaptive MF approach, extending the traditional MF model with new techniques such as data transformation, online learning and adaptive weights. Kamel et al 40 utilize Ant Colony Optimization algorithm to discover high QoS cloud services with minimum costs, combining with Green Computing and ontology techniques, reducing the energy consumption required. Hamza et al 41 proposed a two‐stage context aware service composition algorithm that can select the best QoS service solution based on customer preferences.…”

Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care

“…The main focus of this research is to address how emerging information technologies (IT) may be leveraged and integrated to overcome the aforementioned restrictions faced in LTC servicing. Recently, many studies (Orciuoli & Parente, 2017;Bokhari, Shallal, & Tamandani, 2018;Mansouri, Alti, Roose, & Laborie, 2018;Portugal, Alencar, & Cowan, 2018) have used statistical methods, machine learning or semantic web technologies to improve the intelligence of information systems (IS), including those servicing LTC. This article, published as an Open Access article on January 29, 2021 in the gold Open Access journal, International Journal of Healthcare Information Systems and Informatics (converted to gold Open Access January 1, 2021), is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.…”

“…The main focus of this research is to address how emerging information technologies (IT) may be leveraged and integrated to overcome the aforementioned restrictions faced in LTC servicing. Recently, many studies (Orciuoli & Parente, 2017;Bokhari, Shallal, & Tamandani, 2018;Mansouri, Alti, Roose, & Laborie, 2018;Portugal, Alencar, & Cowan, 2018) have used statistical methods, machine learning or semantic web technologies to improve the intelligence of information systems (IS), including those servicing LTC.…”