TY - GEN
T1 - Minimizing financial cost of scientific workflows under deadline constraints in multi-cloud environments
AU - Gao, Tianyu
AU - Wang, Yongqiang
AU - Wu, Chase Q.
AU - Li, Ruxia
AU - Hou, Aiqin
AU - Xu, Mingrui
N1 - Funding Information:
This research is sponsored by the National Key R&D Program of China under Grant No. 2017YFB1300301, National Nature Science Foundation of China under Grant No. 61472320 and U1609202, and Key Research and Development Plan of Shaanxi Province under Grant No. 2018GY-011 with Northwest University, China, and also by U.S. National Science Foundation under Grant No. CNS-1828123 with New Jersey Institute of Technology.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019
Y1 - 2019
N2 - In recent years, cloud platforms have been rapidly developed and deployed around the globe and many large-scale scientific workflows have been migrated to multiple clouds for cost-effective data analysis. In such cloud-based workflow applications, financial cost is a major concern in addition to traditional performance requirements such as execution time. In this paper, we formulate a workflow mapping problem to minimize the financial cost of deadline-constrained scientific workflows executed in multi-cloud environments, referred to as MinCost-MC, which is shown to be NP-complete. Within a generic three-layer workflow execution framework, we propose a Workflow Mapping algorithm for Financial Cost Optimization, referred to as WMFCO. This algorithm takes in consideration storage requirements, I /O operations, and data transfers to minimize the financial cost of a given workflow within a specified deadline. Extensive simulation results show that WMFCO exhibits a superior performance over existing algorithms in terms of financial cost in multi-cloud environments.
AB - In recent years, cloud platforms have been rapidly developed and deployed around the globe and many large-scale scientific workflows have been migrated to multiple clouds for cost-effective data analysis. In such cloud-based workflow applications, financial cost is a major concern in addition to traditional performance requirements such as execution time. In this paper, we formulate a workflow mapping problem to minimize the financial cost of deadline-constrained scientific workflows executed in multi-cloud environments, referred to as MinCost-MC, which is shown to be NP-complete. Within a generic three-layer workflow execution framework, we propose a Workflow Mapping algorithm for Financial Cost Optimization, referred to as WMFCO. This algorithm takes in consideration storage requirements, I /O operations, and data transfers to minimize the financial cost of a given workflow within a specified deadline. Extensive simulation results show that WMFCO exhibits a superior performance over existing algorithms in terms of financial cost in multi-cloud environments.
KW - Cloud computing
KW - Cost optimization
KW - Workflow mapping
UR - http://www.scopus.com/inward/record.url?scp=85065668175&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065668175&partnerID=8YFLogxK
U2 - 10.1145/3297280.3297293
DO - 10.1145/3297280.3297293
M3 - Conference contribution
AN - SCOPUS:85065668175
SN - 9781450359337
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 114
EP - 121
BT - Proceedings of the ACM Symposium on Applied Computing
PB - Association for Computing Machinery
T2 - 34th Annual ACM Symposium on Applied Computing, SAC 2019
Y2 - 8 April 2019 through 12 April 2019
ER -