Abstract
Deploying a workflow engine as a service on a container cloud environment can improve its service quality and reliability, but auto-scaling of the elastic cloud workflow service doesn’t attract much study attention. Current auto-scaling algorithms oriented to common microservices consider little about the characteristics of a long time and high cost of starting up workflow service, which can easily cause problems such as untimely scaling and excessive scaling. Given this, based on reinforcement learning and semi-Markov decision process (SMDP) modeling, an auto-scaling algorithm for elastic cloud workflow engine is proposed, which enables the cloud workflow service to scale in time, appropriately allocating resources and ensuring service availability. Simulation comparison experiments show that the algorithm automatically scales instances in advance and adapts to changes in traffic through the reinforcement learning SMDP strategy, so that it reduces the violation rate in Service Level Agreements (SLA), and improves the availability of the cloud workflow service.
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Acknowledgements
This work is Supported by the NSFC-Guangdong Joint Fund Project under Grant No. U20A6003;the National Natural Science Foundation of China (NSFC) under Grant No. 61972427; the Research Foundation of Science and Technology Plan Project in Guangdong Province under Grant No. 2020A0505100030.
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Lu, Jb., Yu, Y., Pan, Ml. (2022). Reinforcement Learning-Based Auto-scaling Algorithm for Elastic Cloud Workflow Service. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_28
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DOI: https://doi.org/10.1007/978-3-030-96772-7_28
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