Efficient Resource Allocation for Throughput Maximization in Next-Generation Networks

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) have emerged as the foundation of the next-generation network architecture by introducing great flexibility and network automation capabilities, including automatic response to faults and load changes and programmatic provision of network resources and connections. It has been envisioned that the SDN- and NFV-based next-generation network architecture will play a critical role in providing network services to users, where the desired network services, including data transfer and policy enforcement, are fulfilled by allocating network resources using virtualization technologies. However, the disparity between ever-growing user demands and scarce network resources makes resource allocation exceptionally central to the performance of a network service, because only by effectively allocating these scarce resources can a network service provider satisfy users and maximize the gain from running the service. In this thesis, we study efficient resource allocation for network throughput maximization in next-generation networks, while meeting user resource demands and Quality of Service (QoS) requirements, subject to network resource capacities. This however poses great challenges, namely, (1) how to maximize network throughput, considering that both SDN-enabled switches and links are capacitated, (2) how to maximize the network throughput while taking into account network function and QoS requirements of users, (3) how to dynamically scale and readjust resource allocation for user requests, and (4) how to provision a network service that can satisfy user reliability requirements. To address these challenges, we provide a thorough study of network throughput maximization problems in the context of the next-generation network architecture, by formulating the problems as optimizations problems and developing novel optimization frameworks and algorithms for the problems. Specifically, this thesis makes the following contributions. Firstly, we consider dynamic user request admissions where user requests arrive one by one and the knowledge of future request arrivals is not given as a priori. We develop a novel cost model that accurately captures the usage costs of network resources and propose online algorithms with provable performance guarantees. Secondly, we study the problem of realizing user requests with network function requirements, with the objective of maximizing network throughput, while meeting user QoS requirements, subject to resource capacity constraints. For this problem, we develop two algorithms that strive for the trade-off between the accuracy/quality of a solution and the running time of obtaining the solution. Thirdly, we investigate maximization of network throughput by dynamically scaling network resources while minimizing the overall operational cost of a network. We propose a unified framework for two types of resource scaling {--} vertical scaling and horizontal scaling. Through non-trivial reductions of the problem of concern into several classic problems, we propose an algorithm that has been empirically demonstrated to deliver near-optimal solutions. Fourthly, we deal with the problem of reliability-aware provisioning of network resources for users, with the aim of maximizing network throughput. We devise an approximation algorithm with a logarithmic approximation ratio for the general case of this problem. We also develop constant-factor approximation and exact algorithm for two special cases of the problem, respectively. The formulated problem is a generalization of several classic optimization problems. Finally, in addition to extensive theoretical analyses, we also evaluate the performance of proposed algorithms empirically through experimental simulations based on real and synthetic datasets. Experimental results show that the proposed algorithms significantly outperform existing algorithms

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

ETDP: enhanced topology discovery protocol for software-defined networks

Discovering network elements in a dynamic and optimized manner and being able to contend with ever-growing traffic is a key requirement for current networking environments. In software-defined networks (SDNs), the controller collects the topology information from the data plane and maintains an abstract view of the entire network, which is crucial for the proper functioning of applications and network services. However, there is still the need for an enhanced protocol for automatic discovery and mechanisms of autoconfiguration of network elements according to new policies and business requirements. To overcome this challenge, this paper presents a novel protocol that, unlike existing approaches, enables a distributed layer-2 discovery without the need for previous network configurations or controller knowledge of the network. By using this mechanism, the SDN controller can discover the network view without incurring scalability issues, while taking advantage of the shortest control paths toward each switch. The obtained results show that our enhanced protocol is efficient in terms of time and message load over a wide range of generated networks and outperforms the state-of-the-art techniques.Peer ReviewedPostprint (published version

Softair: Software-defined networking and network function virtualization solutions for 5g cellular systems

One of the main building blocks and major challenges for 5G cellular systems is the design of flexible network architectures, which can be realized by the paradigm of software-defined networking (SDN) and network function virtualization (NFV). Existing commercial cellular systems rely on closed and inflexible hardware-based architectures both at the radio frontend and in the core network. These problems significantly delay the adoption and deployment of new standards, impose great challenges in implementing new techniques to maximize the network capacity and coverage, and prevent provisioning of truly-differentiated services for highly variable traffic patterns. The objective of this thesis is to introduce an innovative software-defined architecture for 5G cellular systems, called SoftAir. First, a detailed overview is provided for priori wireless SDN architecture solutions. Second, the SoftAir architecture is introduced with key design elements. Third, four essential management tools for SoftAir are developed. Last, novel software-defined traffic engineering, enabled by SoftAir, are proposed. Through the synergy of SDN and NFV, SoftAir enables the next-generation cellular networks with the needed flexibility for evolving and adapting to the ever-changing network context, and lays out the foundation for 5G wireless software-defined cellular systems.Ph.D.Ph.D