ePrints

Dynamic Embedding and Quality of Service-Driven Adjustment for Cloud Networks

Lookup NU author(s): Dr Gagangeet Aujla

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

© 2019 IEEE.Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Publication metadata

Author(s): Cao H, Wu S, Aujla GS, Wang Q, Yang L, Zhu H

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Industrial Informatics

Year: 2020

Volume: 16

Issue: 2

Pages: 1406-1416

Print publication date: 01/02/2020

Online publication date: 19/08/2019

Acceptance date: 12/08/2019

ISSN (print): 1551-3203

ISSN (electronic): 1941-0050

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TII.2019.2936074

DOI: 10.1109/TII.2019.2936074

Altmetrics

Share