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Cluster Computing
Published in: Cluster Computing 6/2019

27-02-2018

AI-based software-defined virtual network function scheduling with delay optimization

Authors: Dan Liao, Yulong Wu, Ziyang Wu, Zeyuan Zhu, Wanting Zhang, Gang Sun, Victor Chang

Published in: Cluster Computing | Special Issue 6/2019

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Abstract

AI-based network function virtualization (NFV) is an emerging technique that separates network control functionality from dedicated hardware middleboxes and is virtualized to reduce capital and operational costs. With the advances of NFV and AI-based software-defined networks, dynamic network service demands can be flexibly and effectively accomplished by connecting multiple virtual network functions (VNFs) running on virtual machines. However, such promising technology also introduces several new research challenges. Due to resource constraints, service providers may have to deploy different service function chains (SFCs) to share the same physical resources. Such sharing inevitably forces the scheduling of the SFCs and resources, which consumes computational time and introduces problems associated with reducing the response delay. In this paper, we address this challenge by developing two dynamic priority methods for queuing AI-based VNFs/services to improve the user experience. We account for both transmission and processing delays in our proposed algorithms and achieve a new processing order (scheduler) for VNFs to minimize the overall scheduling delay. The simulation results indicate that the proposed scheme can promote the performance of AI-based VNFs/services to meet strict latency requirements.
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Metadata
Title
AI-based software-defined virtual network function scheduling with delay optimization
Authors
Dan Liao
Yulong Wu
Ziyang Wu
Zeyuan Zhu
Wanting Zhang
Gang Sun
Victor Chang
Publication date
27-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 6/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2124-0

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