Subject Area
Computer Science
Abstract
Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) are two emerging paradigms that enable the feasible and scalable deployment of Virtual Network Functions (VNFs) in commercial-off-the-shelf (COTS) devices, which deliver a range of network services with reduced cost. The deployment of these services requires efficient resource allocation that fulfills the requirements in terms of Quality of Service (QoS) and Service-Level Agreement (SLA) while considering the constraints of the underlying infrastructure, such as maximum latency tolerance and affinity policies.
An optimized resource allocation result can benefit the network in various aspects, such as energy-saving, performance boost, and latency reduction. To achieve a fast, scalable, and dynamic composition of network functions to execute network services, we must first address the resource allocation problem in SDN/NFV-enabled networks, which involves numerous optimization variables resulting from the multidimensional space of system component parameters and states. It is especially nontrivial to address in a complex network as it involves a vast number of optimization variables resulting from the multidimensional space of network component parameters and states. Accordingly, determining the optimal resource allocation is an important and challenging problem to examine in SDN/NFV-enabled networks.
Reinforcement learning is a machine learning branch concerned with how intelligent agents ought to take actions in an environment to maximize the notion of cumulative reward. The agent can learn from the environment without the prior knowledge required and has the potential to outperform any expert. A thoroughly trained agent can make a decision that closely approximates real-time responsiveness. This capability is particularly beneficial in the context of SDN and NFV environments, where the ability to rapidly adapt to changing network conditions, optimize traffic flow, and efficiently allocate resources is critical for maintaining high levels of performance and reliability.
This dissertation contributes significantly to the field of network management by proposing versatile and robust solutions to resource allocation challenges in SDN/NFV environments. Our research paves the way for future advancements, offering a scalable framework that can adapt to the ever-changing dynamics of network technologies and demands. As networks continue to evolve, the principles and methodologies developed in this study hold the promise of shaping the future of network resource management, ensuring that SDN and NFV can fully realize their potential in creating more efficient, reliable, and high-performing network infrastructures.
Degree Date
Spring 5-11-2024
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science
Advisor
Suku Nair
Number of Pages
137
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Su, Jing, "Intelligent Resource Allocation for SDN/NFV-Enabled Networks through Reinforcement Learning" (2024). Computer Science and Engineering Theses and Dissertations. 36.
https://scholar.smu.edu/engineering_compsci_etds/36
