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

On the investigation of cloud-based mobile media environments with service-populating and QoS-aware mechanisms

Recent advances in mobile devices and network technologies have set new trends in the way we use computers and access networks. Cloud Computing, where processing and storage resources are residing on the network is one of these trends. The other is Mobile Computing, where mobile devices such as smartphones and tablets are believed to replace personal computers by combining network connectivity, mobility, and software functionality. In the future, these devices are expected to seamlessly switch between different network providers using vertical handover mechanisms in order to maintain network connectivity at all times. This will enable mobile devices to access Cloud Services without interruption as users move around. Using current service delivery models, mobile devices moving from one geographical location to another will keep accessing those services from the local Cloud of their previous network, which might lead to moving a large volume of data over the Internet backbone over long distances. This scenario highlights the fact that user mobility will result in more congestion on the Internet. This will degrade the Quality of Service and by extension, the Quality of Experience offered by the services in the Cloud and especially multimedia services that have very tight temporal constraints in terms of bandwidth and jitter. We believe that a different approach is required to manage resources more efficiently, while improving the Quality of Service and Media Service Delivery in which services run on localised public Clouds and are capable of populating other public Clouds in different geographical locations depending on service demands and network status. Using an analytical framework, this paper argues that as the demand for specific services increases in a location, it might be more efficient to move those services closer to that location. This will prevent the Internet backbone from experiencing high traffic loads due to multimedia streams and will offer service pr- viders an automated resource allocation and management mechanism for their services

The Future of Mobile Industry

This paper discusses the future of mobile industry along with some of the background leading to the emergence of wireless technology. First, it gives an overview of today’s telecommunication network and the major differences between fixed wired networks and wireless networks. The discussion then focuses on the challenges facing the wireless industry and the way out through aggressive innovation by employing Wireless Intelligence Network (WIN) technology. The paper also discusses some important trends in wireless industry and the customers expectations which are also part of the challenges for the mobile industry. Finally particular reference is made to the developing nations especially Nigeria in the ongoing trends in mobile communication industry

Building Programmable Wireless Networks: An Architectural Survey

In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through co-located data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. The emergence of "programmable wireless networks", that allow greater flexibility, ease of management and configurability, is a step in the right direction to overcome the aforementioned shortcomings of the wireless networks. In this paper, we provide a broad overview of the architectures proposed in literature for building programmable wireless networks focusing primarily on three popular techniques, i.e., software defined networks, cognitive radio networks, and virtualized networks. This survey is a self-contained tutorial on these techniques and its applications. We also discuss the opportunities and challenges in building next-generation programmable wireless networks and identify open research issues and future research directions.Comment: 19 page

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend