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Model-Based Methods in the Biopharmaceutical Process Lifecycle
Verfasser / Verfasserin Kroll, Paul ; Hofer, Alexandra ; Ulonska, Sophia ; Kager, Julian ; Herwig, Christoph
Erschienen in
Pharmaceutical Research, 2017, Jg. 34, H. 12, S. 2596-2613
ErschienenSpringer US, 2017
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)bioprocess / data mining / modelling / monitoring / optimization
Projekt-/ReportnummerIndustrial Methods for Process Analytical Chemistry - From Measurement Technologies to Information Systems (imPACts): 843546 ; Christian Doppler Forschungsgesellschaft: 171
URNurn:nbn:at:at-ubtuw:3-4419 Persistent Identifier (URN)
DOI10.1007/s11095-017-2308-y 
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The Internet of Things (IoT) leads to an ever-growing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fog-based computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.

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CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz