Orchestration and Scheduling of Resources in Softwarized Networks

The Fifth Generation (5G) era is touted as the next generation of mobile networks that will unleash new services and network capabilities, opening up a whole new line of businesses recognized by a top-notch Quality of Service (QoS) and Quality of Experience (QoE) empowered by many recent advancements in network softwarization and providing an innovative on-demand service provisioning on a shared underlying network infrastructure. 5G networks will support the immerse explosion of the Internet of Things (IoT) incurring an expected growth of billions of connected IoT devices by 2020, providing a wide range of services spanning from low-cost sensor-based metering services to low-latency communication services touching health, education and automotive sectors among others. Mobile operators are striving to find a cost effective network solution that will enable them to continuously and automatically upgrade their networks based on their ever growing customers demands in the quest of fulfilling the new rising opportunities of offering novel services empowered by the many emerging IoT devices. Thus, departing from the shortfalls of legacy hardware (i.e., high cost, difficult management and update, etc.) and learning from the different advantages of virtualization technologies which enabled the sharing of computing resources in a cloud environment, mobile operators started to leverage the idea of network softwarization through several emerging technologies. Network Function Virtualization (NFV) promises an ultimate Capital Expenditures (CAPEX) reduction and high flexibility in resource provisioning and service delivery through replacing hardware equipment by software. Software Defined Network (SDN) offers network and mobile operators programmable traffic management and delivery. These technologies will enable the launch of Multi-Access Edge Computing (MEC) paradigm that promises to complete the 5G networks requirements in providing low-latency services by bringing the computing resources to the edge of the network, in close vicinity of the users, hence, assisting the limited capabilities of their IoT devices in delivering their needed services. By leveraging network softwarization, these technologies will initiate a tremendous re-design of current networks that will be transformed to self-managed, software-based networks exploiting multiple benefits ranging from flexibility, programmability, automation, elasticity among others. This dissertation attempts to elaborate and address key challenges related to enabling the re-design of current networks to support a smooth integration of the NFV and MEC technologies. This thesis provides a profound understanding and novel contributions in resource and service provisioning and scheduling towards enabling efficient resource and network utilization of the underlying infrastructure by leveraging several optimization and game theoretic techniques. In particular, we first, investigate the interplay existing between network function mapping, traffic routing and Network Service (NS) scheduling in NFV-based networks and present a Column Generation (CG) decomposition method to solve the problem with considerable runtime improvement over mathematical-based formulations. Given the increasing interest in providing low-latency services and the correlation existing between this objective and the goal of network operators in maximizing their network admissibility through efficiently utilizing their network resources, we revisit the latter problem and tackle it under different assumptions and objectives. Given its complexity, we present a novel game theoretic approach that is able to provide a bounded solution of the problem. Further, we extend our work to the network edge where we promote network elasticity and alleviate virtualization technologies by addressing the problem of task offloading and scheduling along with the IoT application resource allocation problem. Given the complexity of the problem, we propose a Logic-Based Benders (LBBD) decomposition method to efficiently solve it to optimality

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

State of the art of cyber-physical systems security: An automatic control perspective

Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia

SDN workload balancing and QoE control in next generation network infrastructures

The increasing demand of bandwidth, low latency and reliability, even in mobile scenarios, has pushed the evolution of the networking technologies to satisfy new requirements of innovative services. Flexible orchestration of network resources is increasingly being investigated by the research community and by the service operator companies as a mean to easily deploy new remunerative services while reducing capital expenditures and operating expenses. In this regard, the Future Internet initiatives are expected to improve state of the art technologies by developing new orchestrating platforms based on the most prominent enabling technologies, namely, Software Defined Network (SDN) orchestrated Network Function Virtualization (NFV) infrastructure. After introducing the fundamental of the Next Generation Network, formalized as the conceptual Future Internet Platform architecture, the reference scenarios and the proposed control frameworks are given. The thesis discusses the design of two resources management framework of such architecture, targeted, respectively, (i) at the balancing of SDN Control traffic at the network core and (ii) at the user Quality of Experience (QoE) evaluation and control at the network edge. Regarding the first framework, to address the issues related with the adoption of a logically centralized but physically distributed SDN control plane, a discrete-time, distributed, non-cooperative load balancing algorithm is proposed, based on game theory and converged to a specific equilibrium known as Wardrop equilibrium. Regarding the QoE framework, a cognitive approach is presented, aimed at controlling the Quality of Experience (QoE) of the end users by closing the loop between the provided QoS and the user experience feedbacks parameters. QoE Management functionalities are aimed at approaching the desired QoE level exploiting a mathematical model and methodology to identify a set of QoE profiles and an optimal and adaptive control strategy based on a Reinforcement Learning algorithm. For both the proposed solutions, simulation and proof-of-concept implementation results are presented and discussed, to highlight the correctness and the effectiveness of the proposed solutions