KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 15, NO. 3, Mar. 2021 874 Copyright ⓒ 2021 KSII http://doi.org/10.3837/tiis.2021.03.004 ISSN : 1976-7277 Intelligent Massive Traffic Handling Scheme in 5G Bottleneck Backhaul Networks Prohim Tam 1 , Sa Math 1 , and Seokhoon Kim 2* 1 Department of Software Convergence, Soonchunhyang University Asan-si, Chungcheongnam-do 31538, Republic of Korea [e-mail: prohimtam@gmail.com, mathsa98@gmail.com] 2 Department of Computer Software Engineering, Soonchunhyang University Asan-si, Chungcheongnam-do 31538, Republic of Korea [e-mail: seokhoon@sch.ac.kr] * Corresponding author: Seokhoon Kim Received January 10, 2021; revised February 10, 2021; accepted February 18, 2021; published March 31, 2021 Abstract With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput. Keywords: Cloud Computing, Machine Learning, Massive Traffic, Resource Allocation, Software-Defined Networking