Optimization planning techniques with meta-heuristic algorithms in iot: performance and qos evaluation

dc.contributor.authorKoca, Murat
dc.contributor.authorAvcı, İsa
dc.date.accessioned2024-09-29T16:31:02Z
dc.date.available2024-09-29T16:31:02Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBig data analysis used by Internet of Things (IoT) objects is one of the most difficult issues to deal with today due to the data increase rate. Container technology is one of the many technologies available to address this problem. Because of its adaptability, portability, and scalability, it is particularly useful in IoT micro-services. The most promising lightweight virtualization method for providing cloud services has emerged owing to the variety of workloads and cloud resources. The scheduler component is critical in cloud container services for optimizing performance and lowering costs. Even though containers have gained enormous traction in cloud computing, very few thorough publications address container scheduling strategies. This work organizes its most innovative contribution around optimization scheduling techniques, which are based on three meta-heuristic algorithms. These algorithms include the particle swarm algorithm, the genetic algorithm, and the ant colony algorithm. We examine the main advantages, drawbacks, and significant difficulties of the existing approaches based on performance indicators. In addition, we made a fair comparison of the employed algorithms by evaluating their performance through Quality of Service (QoS) while each algorithm proposed a contribution. Finally, it reveals a plethora of potential future research areas for maximizing the use of emergent container technology.en_US
dc.identifier.doi10.35377/saucis...1452049
dc.identifier.endpage186en_US
dc.identifier.issue2en_US
dc.identifier.startpage173en_US
dc.identifier.trdizinid1261208en_US
dc.identifier.urihttps://doi.org/10.35377/saucis...1452049
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1261208
dc.identifier.urihttps://hdl.handle.net/20.500.14619/11094
dc.identifier.volume7en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofSakarya University Journal of Computer and Information Sciences (Online)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleOptimization planning techniques with meta-heuristic algorithms in iot: performance and qos evaluationen_US
dc.typeArticleen_US

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