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

Distributed control in virtualized networks

The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA

Profiled QoE based network controller

Internet evolution follows the customer needs, each algorithm, protocol, architecture, equipment, functionality succeeded when the users perceived a real benefit in using it. Taking into account the impact of customer experience when designing promising and future proof technologies is essential. In this paper we investigate how it is possible to control network resources on the base of the Quality of Experience (QoE), defined as the quality of service perceived by a user when using a specific service. QoE is a subjective measure and typically differs from objective and structured measures of quality of service that are under the service provider's control. We consider the problem of identifying a set of QoE profiles that describes the user behavior when enjoying specific class of services, by analyzing the data related to the users' feedback in different contextual scenarios. We formulated the mathematical model and performed a validation on the base of preliminary field trials

A distributed load balancing algorithm for the control plane in software defined networking

The increasing demand of bandwidth, low latency and reliability, even in mobile scenarios, has pushed the evolution of the networking technologies in order to satisfy the requirements of innovative services. In this context, Software Defined Networking (SDN), namely a new networking paradigm that proposes the decoupling of the control plane from the forwarding plane, enables network control centralization and automation of the network management. In order to address the performance issues related to the SDN Control Plane, this paper proposes a distributed load balancing algorithm with the aim of dynamically balancing the control traffic across a cluster of SDN Controllers, thus minimizing the latency and increasing the overall cluster throughput. The algorithm is based on game theory and converges to a specific equilibrium known as Wardrop equilibrium. Numerical simulations show that the proposed algorithm outperforms a standard static configuration approach

A future internet interface to control programmable networks

current internet infrastructure is still configured and managed manually or adopting a limited level of automation. The Future Internet aims to provide the network resources as a service to ease the process of automatic designing, controlling and supervising the telecommunication infrastructure. A key enabler of the Future Internet is the virtualization of the available resources and of the related functionalities. The widespread of cloud computing, Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies opened the way for a total control of programmable networks. Many open and commercial implementations have adopted this paradigm, but they expose a fragmented set of dissimilar interfaces that often offer similar or even overlapping functionalities. The result is that uncontrolled, open-loop routines and procedures still require a manual intervention. In this paper, we describe an open interface and its reference implementation, to control programmable networks adopting a novel, closed-loop approach based on end-users feedbacks. The proposed interface has been implemented as a Future Internet Generic Enabler named OFNIC

Lyapunov-based design of a distributed wardrop load-balancing algorithm with application to software-defined networking

This paper presents an original discrete-time, distributed, noncooperative load-balancing algorithm, based on mean field game theory, which does not require explicit communications. The algorithm is proven to converge to an arbitrarily small neighborhood of a specific equilibrium among the loads of the providers, known as Wardrop equilibrium. Thanks to its characteristics, the algorithm is suitable for the software-defined networking (SDN) scenario, where service requests coming from the network nodes, i.e., the switches, are managed by the so-called SDN controllers, playing the role of providers. The proposed approach is aimed at dynamically balancing the requests of the switches among the SDN controllers to avoid congestion. This paper also suggests the adoption of SDN Proxies to improve the scalability of the overall SDN paradigm and presents an implementation of the algorithm in a proof-of-concept SDN scenario, which shows the effectiveness of the proposed solution with respect to the current approaches

A Q-Learning based approach to Quality of Experience control in cognitive Future Internet networks

The paper describes an innovative and fully cognitive approach which offers the opportunity to cope with some key limitations of the present telecommunication networks by means of the introduction of a novel architecture design in the perspective of the emerging Future Internet framework. Within this architecture, the Quality of Experience (QoE) Management functionalities are aimed at approaching the desired QoE level of the applications by dynamically selecting the most appropriate Class of Service supported by the network. In the present work, this selection is driven by an optimal and adaptive control strategy based on the renowned Q-Learning algorithm. The proposed dynamic approach differs from the traffic classification approaches found in the literature, where a static assignment of Classes of Service to applications is performed