Name
Title | ||
|---|---|---|
A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-030-00916-8_12 | Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering |
Keywords | Field | DocType |
Cloud computing,Infrastructure as a service,Service composition,Markov decision process,Q-learning | Workflow technology,Computer science,Markov decision process,Q-learning,Workflow application,Big data,Workflow,Distributed computing,Cloud computing,Reinforcement learning | Conference |
Volume | ISSN | Citations |
252 | 1867-8211 | 1 |
PageRank | References | Authors |
0.35 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Wei | 1 | 2 | 2.05 |
| Daniel Kudenko | 2 | 678 | 84.54 |
| Shijun Liu | 3 | 120 | 33.80 |
| Li Pan | 4 | 39 | 18.95 |
| Lei Wu | 5 | 73 | 17.47 |
| Xiangxu Meng | 6 | 308 | 60.76 |