Security-aware job allocation in mobile cloud computing
Authors:
- Piotr Nawrocki,
- Jakub Pajor,
- Bartlomiej Sniezynski,
- Joanna Kolodziej
Abstract
The ultimate goal of Mobile Cloud Computing is to allow users of mobile devices to execute their applications and complex numerical tasks on a broad range of cloud services and resources. One of the most challenging problems in the flow of mobile tasks related to remote cloud services is the security of all aspects of communication, service security and the reliability of cloud resources. In this paper, we developed a new security-aware job flow model for mobile computational clouds. In our model, we defined dedicated algorithm models such as the Filtration Algorithm and Prediction Module to generate an optimal secure system architecture for task and data processing and to ensure optimal cloud resource and service utilization. The robust performance of our model has been demonstrated by experimental analysis. Results of the experiments performed show that our flow model significantly enhances the security level of computations compared to a configuration in which computation time is the major criterion for job processing optimization.
- Record ID
- CUT0552daf6c6dd456fafac48dcc2f150c8
- Publication categories
- ; ;
- Author
- Pages
- 713-719
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 719; Bibliografia (liczba pozycji) - 18; Oznaczenie streszczenia - Abstr.
- Substantive notes
- Publ. w części: CCGRID 2021 Workshops / IWoSeMC 2021 Workshop
- Punktacja MNiSW/MEiN (rozdział) - 20
- Book
- Lefevre Laurent, Laurent Lefevre Patterson Stacy, Stacy Patterson Lee Young Choon Young Choon Lee [et al.] (eds.): CCGrid 2021 : 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 10-13 May 2021 Melbourne, Australia : proceedings, 2021, Los Alamitos [etc.], Institute of Electrical and Electronics Engineers, IEEE, ISBN 978-1-7281-9586-5 (electronic)
- Keywords in English
- security, mobile cloud computing, task allocation, machine learning, adaptation
- DOI
- DOI:10.1109/CCGrid51090.2021.00086 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9499542 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 140
- Additional fields
- Indeksowana w: Web of Science, Scopus, CORE
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT0552daf6c6dd456fafac48dcc2f150c8/
- URN
urn:pkr-prod:CUT0552daf6c6dd456fafac48dcc2f150c8
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.