Abstract
Workflow technology is an efficient means for constructing complex applications which involve multiple applications with different functions. In recent years, with the rapid development of cloud computing, deploying such workflow applications in cloud environment is becoming increasingly popular in many fields, such as scientific computing, big data analysis, collaborative design and manufacturing. In this context, how to schedule cloud-based workflow applications using heterogeneous and changing cloud resources is a formidable challenge. In this paper, we regard the service composition problem as a sequential decision making process and solve it by means of reinforcement learning. The experimental results demonstrate that our approach can find near-optimal solutions through continuous learning in the dynamic cloud market.
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Acknowledgments
The authors would like to acknowledge the support provided by the National Natural Science Foundation of China (61402263, 91546203), the National Key Research and Development Program of China (2016YFB0201405), the Key Research and Development Program of Shandong Province of China (2017CXGC0605), the Shandong Provincial Science and Technology Development Program (2016GGX106001, 2016GGX101008, 2016ZDJS01A09), the Natural Science Foundation of Shandong Province (ZR2014FQ031), the Fundamental Research Funds of Shandong University (2016JC011), the special funds of Taishan scholar construction project, and China Scholarship Council (201606220190).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wei, Y., Kudenko, D., Liu, S., Pan, L., Wu, L., Meng, X. (2018). A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_12
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DOI: https://doi.org/10.1007/978-3-030-00916-8_12
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