Research Area: Software Defined Networks
The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user-s requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.
Keywords:
Artificial intelligence
conventional
load balancing
review
SDN
software-defined networking
systematic
Author(s) Name: Mohammad Riyaz Belgaum; Shahrulniza Musa; Muhammad Mansoor Alam; Mazliham Mohd Su-ud
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2020.2995849
Volume Information: ( Volume: 8) Page(s): 98612 - 98636
