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dc.contributor.author | Grum, M.![]() |
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dc.contributor.author | Bender, B.![]() |
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dc.contributor.author | Alfa, Attahiru Sule![]() |
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dc.contributor.author | Gronau, N.![]() |
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dc.date.accessioned | 2018-08-16T11:54:18Z | |
dc.date.issued | 2018-08 | |
dc.description.abstract | Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time-dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation. | en_ZA |
dc.description.department | Electrical, Electronic and Computer Engineering | en_ZA |
dc.description.embargo | 2019-08-01 | |
dc.description.librarian | hj2018 | en_ZA |
dc.description.sponsorship | Partly funded by the Advanced Sensor Networks SARChI Chair program, co-hosted by the University of Pretoria (UP) and Council for Scientific and Industrial Research (CSIR), through the National Research Foundation (NRF) of South Africa. | en_ZA |
dc.description.uri | https://www.elsevier.com/locate/dss | en_ZA |
dc.identifier.citation | Grum, M., Bender, B., Alfa, A.S. et al. 2018, 'A decision maxim for efficient task realization within analytical network infrastructures', Decision Support Systems, vol. 112, pp. 48-59. | en_ZA |
dc.identifier.issn | 0167-9236 (print) | |
dc.identifier.issn | 1873-5797 (online) | |
dc.identifier.other | 10.1016/j.dss.2018.06.005 | |
dc.identifier.uri | http://hdl.handle.net/2263/66177 | |
dc.language.iso | en | en_ZA |
dc.publisher | Elsevier | en_ZA |
dc.rights | © 2018 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Decision Support Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Decision Support Systems, vol. 112 , pp. 48-59, 2018. doi : 10.1016/j.dss.2018.06.005. | en_ZA |
dc.subject | Analytics | en_ZA |
dc.subject | Architecture concepts | en_ZA |
dc.subject | Cyber-physical systems | en_ZA |
dc.subject | Internet of things (IoT) | en_ZA |
dc.subject | Task realization strategies | en_ZA |
dc.subject | Simulation | en_ZA |
dc.subject | Simulation benchmark | en_ZA |
dc.subject | Scenario-based simulations | en_ZA |
dc.subject | Network infrastructure | en_ZA |
dc.subject | Flexible architectures | en_ZA |
dc.subject | Efficient designs | en_ZA |
dc.subject | Embedded systems | en_ZA |
dc.subject | Decision making | en_ZA |
dc.subject | Network architecture | en_ZA |
dc.title | A decision maxim for efficient task realization within analytical network infrastructures | en_ZA |
dc.type | Postprint Article | en_ZA |