ePrints

A predictive load balancing technique for software defined networked cloud services

Lookup NU author(s): Dr Deepak PuthalORCiD, Professor Raj Ranjan

Downloads

• Accepted version [.pdf]

Licence

This is the authors' accepted manuscript of an article that has been published in its final definitive form by Springer-Verlag Wien, 2019.

For re-use rights please refer to the publisher's terms and conditions.

Abstract

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. With the advent of OpenFlow, the concept of Software-Defined Networking (SDN) becomes much popular. In the past, SDN had often been used for network virtualization; however, with the rise of OpenFlow, which speeds up network performance by separating the control layer from the data layer, SDN can be further used to manage physical network facilities. Currently, some OpenFlow controller providers have already provided users with load balancer packages in their controllers for virtual networks, such as the Neutron package in OpenStack; nevertheless, the existing load balancer packages work in the old fashion that causes extra delay since they poll controllers for every new coming connection. In this paper, we use the wildcard mask to implement the load balance method directly on switches or routers and add a user prediction mechanism to change the range of the wildcard mask dynamically. In this way, the load balance mechanism can be applied conforming to real service situations. In our experiment, we test the accuracies of flow prediction for different predicted algorithms and compare the delay times and balance situations of the proposed method with other load balancers. With the popularity of cloud computing, the demand for cloud infrastructure also increases. As a result, we also apply our load balance mechanism on cloud services and prove that the proposed method can be implemented to varieties of service platforms.

Publication metadata

Author(s): Yang C-T, Chen S-T, Liu J-C, Su Y-W, Puthal D, Ranjan R

Publication type: Article

Publication status: Published

Journal: Computing

Year: 2019

Volume: 101

Issue: 3

Pages: 211-235

Print publication date: 01/03/2019

Online publication date: 26/09/2018

Acceptance date: 20/09/2018

Date deposited: 15/11/2018

ISSN (print): 0010-485X

ISSN (electronic): 1436-5057

Publisher: Springer-Verlag Wien

URL: https://doi.org/10.1007/s00607-018-0665-y

DOI: 10.1007/s00607-018-0665-y

Altmetrics

Share