Performance Evaluation of Congestion Control Algorithm in TCP/IP Differentiated Services During COVID-19 Pandemic Due to Heavy Surge in Real-Time Traffic

Abstract

Numerous nations implemented countrywide lockdowns because of the COVID-19 epidemic, which forced almost all peoples to remain at home. The imposition of confinement restrictions led to an increase in residential users' Internet traffic demands, particularly for distant work, entertainment, commerce, and education. This resulted in changes to the traffic patterns within the Internet core. In this research, we investigate the impact of the countrywide lockdowns on internet traffic based on dscp values by collecting data from class-A, class-B and Class-C ISP.(Three Class-A ISP, Three Class-B ISP and Three Class-C ISP) which leads to heavy congestion in the network and finally the performance valuation is carried out. The overall rise is exponential in nature, we notice that the traffic volume climbed by 15-30% almost in a quarter, which is a significant gain in such a short amount of time. Direct examination of the traffic sources reveals that, although hypergiants continue to account for a sizeable portion of traffic, there has been a greater increase in non-hypergiant traffic as well as in traffic from home-use applications like Web conferencing, VPNs, and gaming. Academic networks show significant overall declines whereas many networks, particularly those serving residential users, pharmaceutical companies, online interaction platform increases in traffic demands. However, we can see significant gains in these networks when we look at applications related to lecturing and remote working. The changes in the nature of use by customers show that there is a sharp difference in application pertaining to usage. Previous to this period, most usage is on YouTube, Facebook and google search. However, after this period most traffic diverted to online platforms such as zoom, google meet, Teams, and others as well as OTT platform comes into the picture. Over the past two decades, there has been a notable shift in user profiles. In the early 2010s, we saw most traffic was inclined on stored video and audio data in continuation with conventional traffic [23, 49, 66], which was followed by content delivery and streaming applications [7, 24, 35, 37, 52], and mobile applications [32, 67] in the 2010s. Even though user profile modifications are a moving target, they usually take years to complete. So, it was possible to stay current, for example, by measurements. The impact of this pandemic created chaotic conditions as most traffic migrated to real time surge data. Thus, the congestion management becomes more complicated as of previous challenges. The ISP forced to provide more bandwidth to real-time traffic which leads to overprovisioning. Thus, an efficient algorithm is required for managing such surge traffic which may especially meet real time traffic as required by most customers. This paper thus focuses on impact of after corona-19 pandemic effect, the shift of traffic by various users and applications the behavior of some existing algorithms in terms of congestion management and finally suggested a balanced method for managing congestion in such heavy surge traffic.