The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computing

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dc.authoridGhaffari, Ali/0000-0001-5407-8629
dc.authorwosidGhaffari, Ali/AAV-3651-2020
dc.contributor.authorAbdalrahman, Aveen Othman
dc.contributor.authorPilevarzadeh, Daniel
dc.contributor.authorGhafouri, Shafi
dc.contributor.authorGhaffari, Ali
dc.date.accessioned2024-05-19T14:50:23Z
dc.date.available2024-05-19T14:50:23Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractFog Computing (FC) provides processing and storage resources at the edge of the Internet of Things (IoT). By doing so, FC can help reduce latency and improve reliability of IoT networks. The energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog environments. Energy consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible resources. To deal with this problem, a binary model based on the combination of the Krill Herd Algorithm (KHA) and the Artificial Hummingbird Algorithm (AHA) is introduced as Binary KHA- AHA (BAHA-KHA). KHA is used to improve AHA. Also, the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling (DVFS) method. The Heterogeneous Earliest Finish Time (HEFT) method is used to discover the order of task flow execution. The goal of the BAHA-KHA model is to minimize the number of resources, the communication between dependent tasks, and reduce energy consumption. In this paper, the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog resources. The results were tested on five different workflows (Montage, CyberShake, LIGO, SIPHT, and Epigenomics). The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA, KHA, PSO and GA algorithms. The BAHA-KHA model has reduced the makespan rate by about 18% and the energy consumption by about 24% in comparison with GA.en_US
dc.identifier.doi10.1007/s42235-023-00389-z
dc.identifier.endpage2464en_US
dc.identifier.issn1672-6529
dc.identifier.issn2543-2141
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85159317077en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2443en_US
dc.identifier.urihttps://doi.org10.1007/s42235-023-00389-z
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5693
dc.identifier.volume20en_US
dc.identifier.wosWOS:000986332000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Singapore Pte Ltden_US
dc.relation.ispartofJournal of Bionic Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectWorkflow Schedulingen_US
dc.subjectFog Computingen_US
dc.subjectInternet Of Thingsen_US
dc.subjectHummingbird Algorithmen_US
dc.subjectKrill Algorithmen_US
dc.titleThe Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computingen_US
dc.typeArticleen_US

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