User-centric power-friendly quality-based network selection strategy for heterogeneous wireless environments

The ‘Always Best Connected’ vision is built around the scenario of a mobile user seamlessly roaming within a multi-operator multi-technology multi-terminal multi-application multi-user environment supported by the next generation of wireless networks. In this heterogeneous environment, users equipped with multi-mode wireless mobile devices will access rich media services via one or more access networks. All these access networks may differ in terms of technology, coverage range, available bandwidth, operator, monetary cost, energy usage etc. In this context, there is a need for a smart network selection decision to be made, to choose the best available network option to cater for the user’s current application and requirements. The decision is a difficult one, especially given the number and dynamics of the possible input parameters. What parameters are used and how those parameters model the application requirements and user needs is important. Also, game theory approaches can be used to model and analyze the cooperative or competitive interaction between the rational decision makers involved, which are users, seeking to get good service quality at good value prices, and/or the network operators, trying to increase their revenue. This thesis presents the roadmap towards an ‘Always Best Connected’ environment. The proposed solution includes an Adapt-or-Handover solution which makes use of a Signal Strength-based Adaptive Multimedia Delivery mechanism (SAMMy) and a Power-Friendly Access Network Selection Strategy (PoFANS) in order to help the user in taking decisions, and to improve the energy efficiency at the end-user mobile device. A Reputation-based System is proposed, which models the user-network interaction as a repeated cooperative game following the repeated Prisoner’s Dilemma game from Game Theory. It combines reputation-based systems, game theory and a network selection mechanism in order to create a reputation-based heterogeneous environment. In this environment, the users keep track of their individual history with the visited networks. Every time, a user connects to a network the user-network interaction game is played. The outcome of the game is a network reputation factor which reflects the network’s previous behavior in assuring service guarantees to the user. The network reputation factor will impact the decision taken by the user next time, when he/she will have to decide whether to connect or not to that specific network. The performance of the proposed solutions was evaluated through in-depth analysis and both simulation-based and experimental-oriented testing. The results clearly show improved performance of the proposed solutions in comparison with other similar state-of-the-art solutions. An energy consumption study for a Google Nexus One streaming adaptive multimedia was performed, and a comprehensive survey on related Game Theory research are provided as part of the work

OFLoad: An OpenFlow-based dynamic load balancing strategy for datacenter networks

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-datacenters, meant to deal with this explosion of data traffic. However this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators’ revenue. In this context there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for datacenter networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity

Una excursió per muntanyes i valls : Gerdhard Ertl i la química de superfícies

El Nobel de Química del 2007 va premiar el treball de Gerdhard Ertl sobre química de superfícies. S'exposen alguns dels seus resultats i com van ser possibles, així com la seva aplicació en catàlisis heterogènies de processos importants

A real-time power monitoring and energy-efficient network/interface selection tool for android smartphones

Energy efficiency in wireless and cellular networks has become one of the most important concerns for both academia and industry due to battery dependence of mobile devices. In this regard, Wireless Network Interface Cards (WNICs) of mobile devices have to be taken into account carefully as they consume an important chunk of the system's total energy. In this paper, we propose a real-time network power consumption profiler and an energy-aware network/interface selection tool for Android-based smartphones. The tool has been freely released on the Android Play Store. The proposed solution reports the power consumption levels of different network interfaces (Wi-Fi and Cellular) by making use of actual packet measurements and precise computations, and enables the devices to handover horizontally/vertically in order to improve the energy efficiency. In this context, widespread analyses have been executed to show the accuracy of the proposed tool. The results demonstrate that the proposed tool is very accurate for any type of IEEE 802.11 wireless or cellular stations, regardless of having different amount of channel utilization, transmission rates, signal strengths or traffic types

360° mulsemedia experience over next generation wireless networks - a reinforcement learning approach

The next generation of wireless networks targets aspiring key performance indicators, like very low latency, higher data rates and more capacity, paving the way for new generations of video streaming technologies, such as 360° or omnidirectional videos. One possible application that could revolutionize the streaming technology is the 360° MULtiple SEnsorial MEDIA (MULSEMEDIA) which enriches the 360° video content with other media objects like olfactory, haptic or even thermoceptic ones. However, the adoption of the 360° Mulsemedia applications might be hindered by the strict Quality of Service (QoS) requirements, like very large bandwidth and low latency for fast responsiveness to the users, inputs that could impact their Quality of Experience (QoE). To this extent, this paper introduces the new concept of 360° Mulsemedia as well as it proposes the use of Reinforcement Learning to enable QoS provisioning over the next generation wireless networks that influences the QoE of the end-users

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided

LearnQoS: a learning approach for optimizing QoS over multimedia-based SDNs

As video-based services become an integral part of the end-users’ lives, there is an imminent need for increase in the backhaul capacity and resource management efficiency to enable a highly enhanced multimedia experience to the endusers. The next-generation networking paradigm offers wide advantages over the traditional networks through simplifying the management layer, especially with the adoption of Software Defined Networks (SDN). However, enabling Quality of Service (QoS) provisioning still remains a challenge that needs to be optimized especially for multimedia-based applications. In this paper, we propose LearnQoS, an intelligent QoS management framework for multimedia-based SDNs. LearnQoS employs a policy-based network management (PBNM) to ensure the compliance of QoS requirements and optimizes the operation of PBNM through Reinforcement Learning (RL). The proposed LearnQoS framework is implemented and evaluated under an experimental setup environment and compared with the default SDN operation in terms of PSNR, MOS, throughput and packet loss

QoS-based routing over software defined networks

Quality of Service (QoS) relies on the shaping of preferential delivery services for applications in favour of ensuring sufficient bandwidth, controlling latency and reducing packet loss. QoS can be achieved by prioritizing important broadband data traffic over the less important one. Thus, depending on the users’ needs, video, voice or data traffic take different priority based on the prevalent importance within a particular context. This prioritization might require changes in the configuration of each network entity which can be difficult in traditional network architecture. To this extent, this paper investigates the use of a QoS-based routing scheme over a Software-Defined Network (SDN). A real SDN test-bed is constructed using Raspberry Pi computers as virtual SDN switches managed by a centralized controller. It is shown that a QoS-based routing approach over SDN generates enormous control possibilities and enables automation