Reinforcement Learning-Based Delay-Aware Path Exploration of Parallelized Service Function Chains

Zhongwei Huang, Dagang Li (), Chenhao Wu and Hua Lu
Additional contact information
Zhongwei Huang: School of Computer Science and Engineering, International Institute of Next Generation Internet, Macau University of Science and Technology, Taipa 999078, Macao
Dagang Li: School of Computer Science and Engineering, International Institute of Next Generation Internet, Macau University of Science and Technology, Taipa 999078, Macao
Chenhao Wu: Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa 999078, Macao
Hua Lu: Guangdong Communication & Network Institute, Guangzhou 510000, China

Mathematics, 2022, vol. 10, issue 24, 1-25

Abstract: The parallel processing of the service function chain (SFC) is expected to provide better low-delay service delivery, because it breaks through the bottleneck of traditional serial processing mode in which service delay increases linearly with the SFC length. However, the provision of parallelized SFC (PSFC) is much more difficult due to the unique construction of PSFCs, inevitable parallelization overhead, and delay balancing requirement of PSFC branches; therefore, existing mechanisms for serial SFC cannot be directly applied to PSFC. After a comprehensive review of recent related work, we find that traffic scheduling mechanisms for PSFCs is still lacking. In this paper, a delay-aware traffic scheduling mechanism (DASM) for PSFCs is proposed. DASM first transforms PSFC into several serial SFCs by releasing the upstream VNF constraints so as to handle them independently while keeping their parallel relations. Secondly, DASM realizes delay-aware PSFC traffic scheduling based on the reinforcement learning (RL) method. To the best knowledge of the authors, this is the first attempt to address the PSFC traffic scheduling problem by transforming them into independent serial SFCs. Simulation results show that the proposed DASM outperforms the advanced PSFCs scheduling strategies in terms of delay balance and throughput.

Keywords: machine learning; reinforcement learning; parallelized service function chains; delay-aware traffic scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/24/4698/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/24/4698/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:24:p:4698-:d:1000213

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().