Introduction
Data centers are one of the most important and essential infrastructures in today’s world. Recent years have seen a rapid growth in the number of cloud computing applications, connected IoT devices. With the large scale deployments of 5G in the near future, there will be even more applications, including more bulk transfers of videos and photos, augmented reality applications and virtual reality applications which take advantage of 5G’s low latency service. All these add to heavy, bulk of data being sent to the data centers and over the backbone network. These traffic have varying quality of service requirements, like low latency, high throughput and high definition video streaming. With the growth of cloud services that use data centers through wide area networks, there has been an increase in the amount of WAN traffic along with data center traffic in data center networks. The interaction of the data center and WAN traffic creates a very interesting scenario with its own challenges to be addressed.
This research project focuses on designing a load balancer for data center networks that is adaptive to the kind of traffic it encounters by learning from the network conditions, and providing low latency and high throughput performance with increased network utilization.