posted on 2019-05-14, 13:46authored byHenrique Yoshikazu Shishido, Júlio Cezar Estrella, Claudio Fabiano Motta Toledo, Stephan Reiff-Marganiec
Scheduling is an important topic to support data security for workflow execution in clouds. Some workflow scheduling algorithms use security services such as authentication, integrity verification, and encryption for all workflow tasks. However, applying security services to no sensitive data does not make sense as no benefit is gained, yet it increases the makespan and monetary costs. In this paper, we introduce five policies for selection of tasks that handle sensitive data. We also propose a workflow scheduling algorithm based on a multi-populational genetic algorithm for minimizing cost while meeting a deadline. Experiments using four workflow applications show that our proposal can minimize both the makespan and cost, while maintaining the security of sensitive data compared to another approach in the literature.
Funding
The authors acknowledge CAPES, FAPESP, CNPq and
USP for the resources provided, USP and UTFPR for the
scholarship to Henrique Yoshikazu Shishido.
History
Citation
2018 IEEE International Conference on Services Computing (SCC), 2018, pp. 233-236
Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics
Source
2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA
Version
AM (Accepted Manuscript)
Published in
2018 IEEE International Conference on Services Computing (SCC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)