TY - JOUR
T1 - Fluctuation-Aware and Predictive Workflow Scheduling in Cost-Effective Infrastructure-as-a-Service Clouds
AU - Li, Weiling
AU - Xia, Yunni
AU - Zhou, Mengchu
AU - Sun, Xiaoning
AU - Zhu, Qingsheng
N1 - Funding Information:
This work was supported in part by the International Joint Project through the Royal Society of the U.K., in part by the National Natural Science Foundation of China under Grant 61611130209, in part by the National Science Foundations of China under Grants 61472051/61702060, in part by the Science Foundation of Chongqing under Grant cstc2017jcyjA1276, in part by the China Postdoctoral Science Foundation under Grant 2015M570770, in part by the Chongqing Postdoctoral Science special Foundation under Grant Xm2015078, in part by the Universities’ Sci-tech Achievements Transformation Project of Chongqing under Grant KJZH17104, in part by FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant 119/2014/A3, and in part by the Chongqing grand RD Projects cstc2017zdcy-zdyf0120 and cstc2017rgzn-zdyf0118.
Publisher Copyright:
© 2013 IEEE.
PY - 2018/9/11
Y1 - 2018/9/11
N2 - Cloud computing is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. In contrast to grids and other traditional high-performance computing systems, clouds provide a customizable infrastructure where scientific workflows can provision desired resources ahead of the execution and set up a required software environment on virtual machines (VMs). Nevertheless, various challenges, especially its quality-of-service prediction and optimal scheduling, are yet to be addressed. Existing studies mainly consider workflow tasks to be executed with VMs having time-invariant, stochastic, or bounded performance and focus on minimizing workflow execution time or execution cost while meeting the quality-of-service requirements. This work considers time-varying performance and aims at minimizing the execution cost of workflow deployed on Infrastructure-as-a-Service clouds while satisfying Service-Level-Agreements with users. We employ time-series-based approaches to capture dynamic performance fluctuations, feed a genetic algorithm with predicted performance of VMs, and generate schedules at run-time. A case study based on real-world third-party IaaS clouds and some well-known scientific workflows show that our proposed approach outperforms traditional approaches, especially those considering time-invariant or bounded performance only.
AB - Cloud computing is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. In contrast to grids and other traditional high-performance computing systems, clouds provide a customizable infrastructure where scientific workflows can provision desired resources ahead of the execution and set up a required software environment on virtual machines (VMs). Nevertheless, various challenges, especially its quality-of-service prediction and optimal scheduling, are yet to be addressed. Existing studies mainly consider workflow tasks to be executed with VMs having time-invariant, stochastic, or bounded performance and focus on minimizing workflow execution time or execution cost while meeting the quality-of-service requirements. This work considers time-varying performance and aims at minimizing the execution cost of workflow deployed on Infrastructure-as-a-Service clouds while satisfying Service-Level-Agreements with users. We employ time-series-based approaches to capture dynamic performance fluctuations, feed a genetic algorithm with predicted performance of VMs, and generate schedules at run-time. A case study based on real-world third-party IaaS clouds and some well-known scientific workflows show that our proposed approach outperforms traditional approaches, especially those considering time-invariant or bounded performance only.
KW - IaaS cloud
KW - quality-of-service (QoS)
KW - scheduling
KW - service-level-agreement
KW - workflow
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U2 - 10.1109/ACCESS.2018.2869827
DO - 10.1109/ACCESS.2018.2869827
M3 - Article
AN - SCOPUS:85053321644
SN - 2169-3536
VL - 6
SP - 61488
EP - 61502
JO - IEEE Access
JF - IEEE Access
M1 - 8463469
ER -