Abstract
ABSTRACT The deployment of datasets in the heterogeneous cloud computing has received increasing attention in current research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy remains a persistent problem. The primary goal of this work is to discover an optimum placement while satisfying the security demand (SD) at the lowest cost. To effectively address this problem, a security-based optimal workflow scheduling (OWS) is proposed for privacy-aware applications over data. During negotiation, the user can submit the SD to the cloud. This work is initialized with list-based heuristics with Particle Swarm Hybridized Red Deer (PSRD). The proposed system can assign tasks for the scientific workflow in the cloud according to the virtual machine (VM). The results show that the workflow schedule provides better security yielding good makespan than the conventional methods with minimum iteration suited for a cloud environment.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
