Machine learning-based optimal load balancing in software-defined networks

Abstract

The global advancement of the Internet of Things (IoT) has poised the existing network traffic for explosive growth. The prediction in the literature shows that in the future, trillions of smart devices will connect to transfer useful information. Accommodating such proliferation of devices in the existing network infrastructure, referred to as the traditional network, is a significant challenge due to the absence of centralized control, making it tedious to implement the device management and network protocol updates. In addition, due to their inherently distributed features, applying machine learning mechanisms in traditional networks is demanding. Consequently, it leads to an imbalanced load in the network that affects the overall network Quality of Service (QoS). Expanding the existing infrastructure and manual traffic control methods are inadequate to cope with the exponential growth of IoT devices. Therefore, an intelligent system is necessary for future networks that can efficiently organize, manage, maintain, and optimize the growing networks. Software-defined network (SDN) has a holistic view of the network and is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers that faces severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (SDN) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, the research was enhanced with a priority scheduling and congestion control algorithm in the standard SDN, named extended SDN (eSDN), which minimized the network congestion and performed better than the existing SDN. However, enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic controller mapping in the network. Often, the same controller gets overloaded, leading to a single point of failure. Our exhaustive literature review shows that the majority of proposed solutions are based on static controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among controllers in real-time, eventually increasing the network latency. Often, the switch experiences a traffic burst, and consequently, the corresponding controller might overload. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static controller to neutralize the on-the-fly traffic burst. Addressing the above-mentioned issues demands research critical to improving the QoS in load balancing, latency minimisation, and network reliability for next- generation networks. Our novel dynamic controller mapping algorithm with multiple- controller placement in the SDN is critical in solving the identified issues. In the dynamic controller approach (dSDN), the controllers are mapped dynamically as the load fluctuates. If any controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. In addition, our novel approach adds more intelligence to the network with a Temporal Deep Q Learning (tDQN) approach for dynamic controller mapping when the flow fluctuates. In this technique, a multi-objective optimization problem for flow fluctuation is formulated to dynamically divert the traffic to the best-suited controller. The formulated technique is placed as an agent in the network controller to take care of all the routing decisions, which can solve the dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. Extensive simulation results show that the novel approach proposed in this thesis solves dynamic flow mapping by maintaining a balanced load among controllers and outperforms the existing traditional networks and SDN with priority scheduling and congestion control. Compared to traditional networks, tDQN provides a 47.48% increase in throughput, a 99.10% reduction in delay and a 97.98% reduction in jitter for heavy network traffic. The thesis also presents a few future research directions as possible extensions of the current work for further enhancement.Doctor of Philosoph