Stammdaten

Titel: Multi-objective scheduling of extreme data scientific workflows in Fog
Untertitel:
Kurzfassung:

The concept of “extreme data” is a recent re-incarnation of the “big data” problem, which is distinguished by the massive amounts of information that must be analyzed with strict time requirements. In the past decade, the Cloud data centers have been envisioned as the essential computing architectures for enabling extreme data workflows. However, the Cloud data centers are often geographically distributed. Such geographical distribution increases offloading latency, making it unsuitable for processing of workflows with strict latency requirements, as the data transfer times could be very high. Fog computing emerged as a promising solution to this issue, as it allows partial workflow processing in lower-network layers. Performing data processing on the Fog significantly reduces data transfer latency, allowing to meet the workflows’ strict latency requirements. However, the Fog layer is highly heterogeneous and loosely connected, which affects reliability and response time of task offloading. In this work, we investigate the potential of Fog for scheduling of extreme data workflows with strict response time requirements. Moreover, we propose a novel Pareto-based approach for task offloading in Fog, called Multi-objective Workflow Offloading (MOWO). MOWO considers three optimization objectives, namely response time, reliability, and financial cost. We evaluate MOWO workflow scheduler on a set of real-world biomedical, meteorological and astronomy workflows representing examples of extreme data application with strict latency requirements.

Schlagworte: Scheduling, Scientific workflows, Fog computing, Task offloading, Monte-Carlo simulation, Multi-objective optimization
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Future Generation Computer Systems
Future Generation Computer Systems
zur Publikation
 ( Elsevier; )
Erscheinungsdatum: 07.01.2020
Titel der Serie: -
Bandnummer: -
Heftnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 1 - 37

Identifikatoren

ISBN: -
ISSN: 0167-739X
DOI: http://dx.doi.org/10.1016/j.future.2019.12.054
AC-Nummer: -
Homepage: https://www.sciencedirect.com/science/article/pii/S0167739X19309197?via%3Dihub
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020  Klagenfurt am Wörthersee
Österreich
  -3699
   martina.steinbacher@uni-klu.ac.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 1020 - Informatik
Forschungscluster Kein Forschungscluster ausgewählt
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Distributed Multimedia Systems

Kooperationen

Organisation Adresse
Vienna University of Technology
Karlsplatz 13
1040  Wien
Österreich
Karlsplatz 13
AT - 1040  Wien

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