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 - 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 -