Advanced Search

Show simple item record

dc.contributor.authorVakili, Asrin
dc.contributor.authorAl-Khafaji, Hamza Mohammed Ridha
dc.contributor.authorDarbandi, Mehdi
dc.contributor.authorHeidari, Arash
dc.contributor.authorJafari Navimipour, Nima
dc.contributor.authorÜnal, Mehmet
dc.date.accessioned2024-09-04T10:45:14Z
dc.date.available2024-09-04T10:45:14Z
dc.date.issued2024en_US
dc.identifier.citationVakili, A., Al‐Khafaji, H. M. R., Darbandi, M., Heidari, A., Jafari Navimipour, N., & Unal, M. (2024). A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework. Concurrency and Computation: Practice and Experience.en_US
dc.identifier.issn1532-0626
dc.identifier.urihttps://hdl.handle.net/20.500.12900/385
dc.description.abstractCloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP-hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy-saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.en_US
dc.language.isoengen_US
dc.publisherWILEYen_US
dc.relation.isversionof10.1002/cpe.8091en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCloud computingen_US
dc.subjectGrey wolf optimizationen_US
dc.subjectMapReduceen_US
dc.titleA new service composition method in the cloud-based Internet of things environment using a grey wolf optimization algorithm and MapReduce frameworken_US
dc.typearticleen_US
dc.departmentİstanbul Atlas Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authoridhttps://orcid.org/0000-0003-4279-8551en_US
dc.contributor.institutionauthorHeidari, Arash
dc.identifier.volume36en_US
dc.identifier.issue16en_US
dc.relation.journalCONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record