Software-Defined Networking (SDN) has received, in the last years, significant interest from the academic and the industrial communities alike. The decoupled control and data planes found in an SDN allows for logically centralized intelligence in the control plane and generalized network hardware in the data plane. Although the current SDN ecosystem provides a rich support for wired packet-switched networks, the same cannot be said for wireless networks where specific radio data-plane abstractions, controllers, and programming primitives are still yet to be established. In this work, we present a set of programming abstractions modeling the fundamental aspects of a wireless network, namely state management, resource provisioning, network monitoring, and network reconfiguration. The proposed abstractions hide away the implementation details of the underlying wireless technology providing programmers with expressive tools to control the state of the network. We also present a Software-Defined Radio Access Network Controller for Enterprise WLANs and a Python--based Software Development Kit implementing the proposed abstractions. Finally, we experimentally evaluate the usefulness, efficiency and flexibility of the platform over a real 802.11-based WLAN.

In this paper we discuss the evolution of mobility management mechanisms in mobile networks. We emphasize problems with current mobility management approaches in case of very high dense and heterogeneous networks. The main contribution of the paper is a discussion on how the Software-Defined Networking (SDN) technology can be applied in mobile networks in order to efficiently handle mobility in the context of future mobile networks (5G) or evolved LTE. The discussion addresses the most important problems related to mobility management like preservation of session continuity and scalability of handovers in very dense mobile networks. Three variants of SDN usage in order to handle mobility are described and compared in this paper. The most advanced of these variants shows how mobility management mechanisms can be easily integrated with autonomie management mechanisms, providing much more advanced functionality than is provided now by the SON approach. Such mechanisms increase robustness of the handover and optimize the usage of wireless and wired mobile network resources.

In this paper we present a new multilevel clustering algorithm for Vehicular Ad-Hoc Networks (VANET), which we will call the Density Based Clustering (DBC) algorithm. Our solution is focused on the formation of stable, long living clusters for reliable communication. Cluster formation is based on complex clustering metric which takes into account density of connection graph, link quality and traffic conditions. Tests performed in the simulation environment composed from VanetMobiSim and JiST/SWANS have shown that algorithm performs better than the popular classic approach (the Lowest Id algorithm [1]) - the clusters stability is significantly increased.

Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.

The Software Defined Networks (SDNs) and Network Functions Virtualisation (NFVs), as recent separate research and development trends have the roots in programmable / active network technologies and standards developed a decade ago. In particular, they are associated with the decoupling of forwarding from control and hardware from networking software, using open interfaces to connectivity resources. The next phase of R&D would involve novel integration and use of all connectivity, storage and processing resources under new management interacting with control systems for provisioning of on-demand networking and services with continuous update of features. This brings into focus a relatively new and key topics for the next decade: what and how to create the conditions for effective and continuous updating and changing the networking functions without reinventing each time architectural aspects and related components (e.g. Softwarization of Future Networks and Services or Programmable Enabled Networks). This paper presents motivation, architecture and the key challenges in realising such programmable enabled networks as the next generation Software Defined Networks focusing on its management plane.

Network slicing technology will influence the way in which new networking solutions will be designed and operated. So far, network slicing is often linked with 5G networks, but this approach can be used to deploy any communications network(s) over a common infrastructure. The concept is still a subject of intensive research and standardization. From the point of view of network or service operator, it is necessary to define fundamental quantitative indicators for performance evaluation of the network slicing. Such parameters are often called Key Performance Indicators (KPIs). Network slicing KPIs should deal with network slicing run-time and life-cycle management and orchestration. The paper proposes a set of KPIs for network slicing taking into account the 5G network specifics.