Leveraging synergy of SDWN and multi-layer resource management for 5G networks

Fifth-generation (5G) networks are envisioned to predispose service-oriented and flexible edge-to-core infrastructure to offer diverse applications. Convergence of software-defined networking (SDN), software-defined radio (SDR), and virtualization on the concept of software-defined wireless networking (SDWN) is a promising approach to support such dynamic networks. The principal technique behind the 5G-SDWN framework is the separation of control and data planes, from deep core entities to edge wireless access points. This separation allows the abstraction of resources as transmission parameters of users. In such user-centric and service-oriented environment, resource management plays a critical role to achieve efficiency and reliability. In this paper, we introduce a converged multi-layer resource management (CML-RM) framework for SDWN-enabled 5G networks, that involves a functional model and an optimization framework. In such framework, the key questions are if 5G-SDWN can be leveraged to enable CML-RM over the portfolio of resources, and reciprocally, if CML-RM can effectively provide performance enhancement and reliability for 5G-SDWN. In this paper, we tackle these questions by proposing a flexible protocol structure for 5G-SDWN, which can handle all the required functionalities in a more cross-layer manner. Based on this, we demonstrate how the proposed general framework of CML-RM can control the end-user quality of experience. Moreover, for two scenarios of 5G-SDWN, we investigate the effects of joint user-association and resource allocation via CML-RM to improve performance in virtualized networks

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited