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Lookup NU author(s): Khaled Alwasel,
Dr Deepak Puthal,
Professor Philip James,
Dr Graham Morgan,
Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2020 Elsevier Inc.Software-defined networking (SDN) has evolved as an approach that allows network administrators to program and initialize, control, change and manage networking components (mostly at L2-L3 layers) of the OSI model. SDN is designed to address the programmability shortcomings of traditional networking architectures commonly used in cloud datacenters (CDC). Deployment of SDN solutions have demonstrated significant improvements in areas such as flow optimization and bandwidth allocation in a CDC. However, the benefits are significantly less explored when considering Software-Defined Wide Area Networks (SD-WAN) architectures in the context of delivering solutions by networking multiple CDCs. To support the testing and bench-marking of data-driven applications that rely on data ingestion and processing (e.g., Smart Energy Cloud, Content Delivery Networks) across multiple cloud datacenters, this paper presents the simulator, IoTSim-SDWAN. To the best of our knowledge, IoTSim-SDWAN is the first simulator that facilitates the modeling, simulating, and evaluating of new algorithms, policies, and designs in the context of SD-WAN ecosystems and SDN-enabled multiple cloud datacenters. Finally, IoTSim-SDWAN simulator is evaluated for network performance and energy to illustrate the difference between classical WAN and SD-WAN environments. The obtained results show that SD-WAN surpasses the classical WAN in terms of accelerating traffic flows and reducing power consumption.
Author(s): Alwasel K, Jha DN, Hernandez E, Puthal D, Barika M, Varghese B, Garg SK, James P, Zomaya A, Morgan G, Ranjan R
Publication type: Article
Publication status: Published
Journal: Journal of Parallel and Distributed Computing
Print publication date: 01/09/2020
Online publication date: 05/05/2020
Acceptance date: 11/04/2020
Date deposited: 09/07/2020
ISSN (print): 0743-7315
ISSN (electronic): 1096-0848
Publisher: Academic Press Inc.
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