Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/8790
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dc.contributor.authorÖz, Dindaren_US
dc.contributor.authorÖz, Işılen_US
dc.date.accessioned2020-07-18T08:31:25Z-
dc.date.available2020-07-18T08:31:25Z-
dc.date.issued2021-03en_US
dc.identifier.issn0920-8542-
dc.identifier.issn1573-0484-
dc.identifier.urihttps://doi.org/10.1007/s11227-020-03369-w-
dc.identifier.urihttps://hdl.handle.net/11147/8790-
dc.description.abstractAs the distributed computing systems have been widely used in many research and industrial areas, the problem of allocating tasks to available processors in the system efficiently has been an important concern. Since the problem is proven to be NP-hard, heuristic-based optimization techniques have been proposed to solve the task allocation problem. Particularly, the current cloud-based systems have been grown massively requiring multiple features like lower cost, higher reliability, and higher throughput; therefore, the problem has become more challenging and approximate methods have gained more importance. Migrating birds optimization (MBO) algorithm offers successful solutions, especially for quadratic assignment problems. Inspired by the movement of the birds, it exhibits good results by its population-based approach . Since the algorithm needs to deal with many individuals in the population, and the neighbor solution generation phase takes substantial time for large problem instances, we need parallelism to have execution time improvements and make the algorithm practical for large-scale problems. In this work, we propose a scalable parallel implementation of the MBO algorithm, PMBO, for the multi-objective task allocation problem. We redesigned the implementation of the MBO algorithm so that its computationally heavy independent tasks are executed concurrently in separate threads. We compare our implementation with three parallel island-based approaches. The experimental results demonstrate that our implementation exhibits substantial solution quality improvements for difficult problem instances as the computing resources, namely parallelism, increase. Our scalability analysis also presents that higher parallelism levels offer larger solution improvement for the PMBO over the island-based parallel implementations on very hard problem instances.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofJournal of Supercomputingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParallel algorithmen_US
dc.subjectCombinatorial optimizationen_US
dc.subjectTask allocation problemen_US
dc.subjectMigrating birds optimizationen_US
dc.titleScalable parallel implementation of migrating birds optimization for the multi-objective task allocation problemen_US
dc.typeArticleen_US
dc.authorid0000-0002-8310-1143en_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume77-
dc.identifier.issue3-
dc.identifier.startpage2689-
dc.identifier.endpage2712-
dc.identifier.wosWOS:000544846600002-
dc.identifier.scopusSCOPUS:2-s2.0-85087387724-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s11227-020-03369-w-
dc.relation.doi10.1007/s11227-020-03369-wen_US
dc.coverage.doi10.1007/s11227-020-03369-wen_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextembargo_20240101-
crisitem.author.deptDepartment of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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