Energy and Congestion Awareness Traffic Scheduling in Hybrid Software-Defined Network with Flow Splitting

Citation

Lim, C. S. and Tan, Saw Chin and Imanuel, A. and Teh, J. S. L. and Baderulhisham, N. Q. (2023) Energy and Congestion Awareness Traffic Scheduling in Hybrid Software-Defined Network with Flow Splitting. Lecture Notes in Computer Science, 14162. pp. 621-633. ISSN 0302-9743

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Abstract

Software-Defined Networking (SDN) has received a lot of interest in recent years because of its benefits over network controllability. Nonetheless, the deployment of SDN in legacy networks is likely to take months or years due to funding constraints. Traffic scheduling that involve flow splitting provides the flexibility for traffic flow. It is able to minimize the maximum link capacity of a network and to reduce the traffic congestion in the network. The majority of the studies focus on how to balance the flows coming out of the conventional nodes and how to partition the flows that gather at the SDN nodes so that the maximum link usage of the entire network can be reduced. Energy efficiency of a network are important to save cost and energy. During traffic scheduling, the energy consumption of a traffic flow should be considered. As a result, in hybrid SDN, we offer a heuristic approach for energy and congestion awareness traffic scheduling with flow splitting. We first define the aforementioned issue in an Integer Linear Programming (ILP) model, and then we assess the suggested ILP model and heuristic algorithm in terms of solution quality and processing time. The findings indicate that with polynomial time complexity, our suggested approach retains its overall soundness.

Item Type: Article
Uncontrolled Keywords: Energy-awareness congestion hybrid Software Defined Networking
Subjects: H Social Sciences > HC Economic History and Conditions
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 Oct 2023 07:25
Last Modified: 31 Oct 2023 07:25
URII: http://shdl.mmu.edu.my/id/eprint/11789

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