Title
Deadline distribution strategies for scientific workflow scheduling in commercial clouds.
Abstract
Commercial clouds have become a viable platform for performing a significant range of large scale scientific analyses - due to the offerings of elasticity, specialist hardware, software infrastructure and pay-as-you-go cost model. Such clouds represent a low upfront capital cost alternative to the use of dedicated eScience infrastructure. However, there are still significant technical hurdles associated with obtaining the best performance for the cost - it is easy to provision commercial clouds inefficiently resulting in great and potentially unanticipated expense. In this paper we introduce a new heuristic scheduling algorithm Deadline Distribution Ratio (DDR) to address the workflow scheduling problem with the objectives of minimizing the cost of Cloud computing resources while satisfying a given deadline. Within this context, we also investigate a range of different deadline distribution strategies and their effect on the overall scheduling performance. We then compare the DDR algorithm against three other published algorithms, using five different scientific workflows generated using the pegasus workflow generator, on a CloudSim simulation that implements a pricing model based on AWS. In general, the DDR algorithm returns the lowest costs across the majority of deadlines and workflows, while maintaining a high scheduling success rate.
Year
DOI
Venue
2016
10.1145/2996890.2996905
International Conference on Utility and Cloud Computing
Keywords
Field
DocType
Scientific Workflow, Scheduling, Deadline Distribution, Cloud Computing
Capital cost,Fair-share scheduling,Scheduling (computing),Computer science,Earliest deadline first scheduling,Dynamic priority scheduling,Workflow,CloudSim,Cloud computing,Distributed computing
Conference
ISSN
ISBN
Citations 
2373-6860
978-1-5090-4467-2
1
PageRank 
References 
Authors
0.36
12
3
Name
Order
Citations
PageRank
Vahid Arabnejad1303.13
Kris Bubendorfer234129.28
Bryan Ng310020.84