Research Article
A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment
@INPROCEEDINGS{10.1007/978-3-030-00916-8_12, author={Yi Wei and Daniel Kudenko and Shijun Liu and Li Pan and Lei Wu and Xiangxu Meng}, title={A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={10}, keywords={Cloud computing Infrastructure as a service Service composition Markov decision process Q-learning}, doi={10.1007/978-3-030-00916-8_12} }- Yi Wei
Daniel Kudenko
Shijun Liu
Li Pan
Lei Wu
Xiangxu Meng
Year: 2018
A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_12
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.
