Advanced cost-aware Max-Min workflow tasks allocation and scheduling in cloud computing systems

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dc.authoridFazea, Yousef/0000-0003-3544-2434
dc.authoridKaosar, Mohammed/0000-0003-1101-3264
dc.contributor.authorRaeisi-Varzaneh, Mostafa
dc.contributor.authorDakkak, Omar
dc.contributor.authorFazea, Yousef
dc.contributor.authorKaosar, Mohammed Golam
dc.date.accessioned2024-09-29T15:51:13Z
dc.date.available2024-09-29T15:51:13Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractCloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max-Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max-Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max-Min Algorithm outperforms the traditional Max-Min, Min-Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)en_US
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK).en_US
dc.identifier.doi10.1007/s10586-024-04594-1
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.scopus2-s2.0-85197887262en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10586-024-04594-1
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3932
dc.identifier.wosWOS:001258039900003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdvanced Max-Min algorithmen_US
dc.subjectTask schedulingen_US
dc.subjectCloud computingen_US
dc.subjectTask allocationen_US
dc.subjectMakespanen_US
dc.subjectCost awareen_US
dc.titleAdvanced cost-aware Max-Min workflow tasks allocation and scheduling in cloud computing systemsen_US
dc.typeArticleen_US

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