RT Journal Article
T1 Multi-objective scheduling of extreme data scientific workflows in Fog
A1 De Maio, Vincenzo
A1 Kimovski, Dragi
AB 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.
PB Elsevier B.V.
SN 0167-739X
YR 2020
FD 2020-05
LK https://hdl.handle.net/10016/30177
UL https://hdl.handle.net/10016/30177
LA eng
NO ASPIDE: Exascale programIng models for extreme data processing
NO The work described in this paper has been funded throughthe Rucon project (Runtime Control in Multi Clouds), Austria, FWF Y 904 START-Programme 2015, Austria, the H2020 ASPIDE project, European Commision, grant agreement No 801091 and the ATOMICFOG project (Austria), OEAD AT/MK 08/2018
DS e-Archivo
RD 18 jul. 2024