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Lookup NU author(s): Faris Llwaah, Dr Jacek CalaORCiD, Dr Nigel Thomas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Big data analysis has become a vital tool in many disciplines. Due to its intensive nature, big data analysis is often performed in cloud computing environments. Cloud computing offers the potential for large scale parallelism and scalable provision. However, determining an optimal deployment can be an expensive operation and therefore some form of prediction of performance prior to deployment would be extremely useful. In this paper we explore the deployment of one complex such problem, the NGS pipeline. We use provenance execution data to populate models simulated in WorkflowSim and CloudSim. This allows us to explore different scenarios for runtime properties.
Author(s): Llwaah F, Cala J, Thomas N
Editor(s): Fiems, D; Paolieri, M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 13th European Performance Engineering Workshop (EPEW)
Year of Conference: 2016
Pages: 141-155
Print publication date: 16/09/2016
Online publication date: 16/09/2016
Acceptance date: 15/07/2016
Date deposited: 31/10/2016
ISSN: 0302-9743
Publisher: Springer
URL: http://dx.doi.org/10.1007/978-3-319-46433-6_10
DOI: 10.1007/978-3-319-46433-6_10
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783319464336
