Multipath scheduling has been a hot research topic in recent years thanks to the development of multipath transport protocols (MP-TCP and MP-QUIC) and the wide deployment of 5G infrastructures. These schedulers take advantage of multiple physical interfaces on end-user devices, thus improving performance and reliability. While Peekaboo, the state-of-the-art scheduler, seems to provide the best performance in some specific environments, some evidence is that other existing schedulers may outperform it in more realistic network conditions. Based on this observation, we proposed GADaM, stands for Generic Adaptive Deep-learning based Multipath scheduler selector. GADaM’s role is to select the most appropriate scheduler under specific network conditions dynamically. The proposed paradigm proved its effectiveness in simulations, but its behavior in more realistic (typically mobile) environments remains largely unknown. This paper presents an experimental evaluation of GaDAM in real-world scenarios performed in an urban area near Paris, France. We design and implement a framework to serve this purpose, which takes mobility and fairness into consideration. Our results confirm the advantage of GaDAM’s approach compared to the deterministic scheduler selection approach.

The 4G standard Long Term Evolution-Advanced (LTE-A) has been deployed in many countries. Now, technology is evolving towards the 5G standard since it is expecting to start its service in 2020. The 5G cellular networks will mainly contain in cloud computing and primarily Quality of Service (QoS) parameters (e.g. delay, loss rate, etc.) influence the cloud network performance. The impact of user perceived Quality of Experience (QoE) using multimedia services, and application significantly relies on the QoS parameters. The key challenge of 5G technology is to reduce the delay less than one millisecond. In this paper, we have described a method that minimizes the overall network delay for multimedia services; which are constant bit rate (VoIP) and variable bit rate (video) traffic model. We also proposed a method that measures the user's QoE for video streaming traffic using the network QoS parameters, i.e. delay and packet loss rate. The performance of proposed QoE method is compared with QoV method, and our proposed QoE method performs best by carefully handle the impact of QoS parameters. The results show that our described method successfully reduces the overall network delays, which result to maximize the user's QoE.

HTTP video streaming becomes a main contributor in the ever increasing Internet traffic. It is not an easy task for the network service provider to guarantee the best user's Quality of Experience (QoE) in diverse networks with different access technologies. This requires an adaptive method that dynamically adapts the video quality service over HTTP according to time varying network conditions. In this paper, a client-based rate adaptive method is proposed that dynamically selects the appropriate video quality according to network conditions and user's device properties. The proposed method considers three important Quality of Service (QoS) factors that regulate the user's QoE for video streaming over HTTP, which are: Bandwidth, Buffer, and dropped Frame rate (BBF). The network bandwidth significantly affects the video service, as it directly reduces the client buffering that may result in pausing or stalling during video streaming. The proposed BBF method efficiently deals with sudden drop of network bandwidth by using the new bandwidth metric, and reduces its impact on the buffer level of the end user. The buffer length plays a vital role to handle the dynamic change in bandwidth. The dropped frame rate (fps) is another influential factor that minimizes the user's QoE. The proposed BBF algorithm is evaluated with different buffer length, and it is compared to Adobe's OSMF adaptive method.

The quality assessment of multimedia services as perceived by the end-user is a challenging task. In a controlled environment, it is easy to measure the user's Quality of Experience (QoE) against the influence of controlled network parameters. However, in an uncontrolled environment, it is difficult to measure the Quality of Service (QoS) perceived by end-users, due to the unpredictable behaviour of networks. Crowd-sourcing is an emerging technique that can be employed to measure the QoE at the end user, but in an uncontrolled environment. In this paper, we present a tool implementing a crowd-sourcing framework, that measures the QoE of online video streaming, as perceived by end-users. The tool also measures important QoS network parameters in real-time (packet loss, delay, jitter and throughput), retrieves system information (memory, processing power etc.), and other properties of the end-user's system. The proposed approach provides the opportunity to explore the user's quality perception in a wider domain.

In multimedia services, user's perceived quality of service is very important as compared to other end-to-end network factors. With the growth of emerging technologies, it becomes necessary for network service providers (both in the wired and wireless domains) to consider all aspects of network elements, not only to offer a better Quality of Service (QoS) but also to achieve higher end-users satisfaction. The analysis of users' profile provides vital information, which can help service providers in managing their resources efficiently, by analyzing users' behavior and expectation. In this paper, we setup a testbed to investigate the various factors that contribute to the Quality of Experience (QoE), in the context of video streaming delivery over wireless and wired networks. The comprehensive study of users profile provides significant insights on all metrics that influence the QoE (network parameters and video characteristics). Wireless and wired networks have different infrastructure aspects (reliability, availability etc.) but the analysis and evaluations of users' profile are equally important for both networks. Our analytical study gives an opportunity for network service providers to obtain high user satisfaction by providing a service level that matches customers' usage patterns and expectations.

Emerging multimedia services are spreading significantly thanks to the availability of high speed wireless networks. Network operators are facing the challenges to accommodate these services (e.g. Skype, interactive gaming, and other multimedia applications) with high user satisfaction. This paper presents a Quality of Experience (QoE) driven approach to allocate the radio resources in Long Term Evolution (LTE) wireless networks, in order to get higher user satisfaction. The main objective is to enhance the user experience while using the multimedia services such as VoIP, by jointly considering the user QoE and available wireless radio resources in the LTE-Advanced (LTE-A) network. Specifically, we propose a new downlink scheduling algorithm known as “QoE Scheme” for multimedia VoIP service in LTE-A networks. The main advantage of the proposed QoE Scheme is its measurement of user satisfaction and its feedback mechanism to the scheduler at evolving NodeB (eNodeB) in order to make the scheduling decision.

Network lifetime has become the key characteristic for evaluating sensor networks in an application-specific way. In this paper, we focus on Mobile Wireless Sensor Networks, which are specific opportunistic networks because of their poor connectivity among the mobile sensors, and thus it is difficult to form a well connected mesh network for transmitting data through end-to-end connections from the sensors to the sink. We propose EXLIOSE (Novel approach to EXtending network LIfetime in Opportunistic SEnsor Networks), a routing protocol that focuses on maximizing network lifetime while keeping high delivery statistics. EXLIOSE is based on a novel routing metric that uses the history of encounters between nodes, and the nodal residual energy. Simulation results show that our approach is able to extend network lifetime and achieve good delivery statistics, as compared to state-of-the-art solutions.

For years, wireless network systems have been trying to satisfy end-users and support high quality multimedia applications such as Mobile TV, VoIP, etc. Combining wireless networks with multimedia content distribution needs efficient routing protocols. We develop in this paper a new routing protocol, namely DOQAR (Dynamic Optimized QoE Adaptive Routing), to improve the user perception and optimize the usage of network resources. In our end-to-end model, smartphone users connect to content servers in a wired network across a wireless access network. In order to evaluate the QoE, we use a Multi-Layer Perception-based (MLP) method. Experimental results show a significant performance against other traditional routing protocols.

HTTP has been the most popular internet protocol for 30 years. Until recently, its role has been limited to a traditional transfer of hypertext documents. However, its flexibility and interoperability cause it to be progressively involved in a much wider range of applications, from video and audio streaming to email, chat and documents editing. Understanding the behavior of modern Web applications is a crucial step to apply QoS or security policies on this traffic. This paper studies 20 popular, Web applications that are representative of 12 application types. We describe a method to isolate and capture browser-generated traffic and plot time series with an RRDTool database. We show that modern Web applications present very diverse traffic patterns, and propose a description and classification of these patterns.

Driven by the demographic variation in population and degradation of health care quality, we have witnessed in the recent years the explosion of healthcare applications market in order to provide high quality care. Wireless Sensor Networks (WSN) seem to be an attractive technology to deploy this type of applications thanks to their advantages of simplicity of deployment, safe of use and reduced installation cost. In this paper we present a specific medical application on which we work in collaboration with the Henri Mondor University Hospital Center in France. This application aims to monitor patients' physiological parameters and track their localizations within the hospital. We describe in this paper tests performed in our lab and analyze the first results of implementation of a sensor network platform.

Tools to measure Internet properties usually assume the existence of just one single path from a source to a destination. However, load-balancing capabilities, which create multiple active paths between two end-hosts, are available in most contemporary routers. This paper extends Paris trace route and proposes an extensive characterization of multipath routing in the Internet. We use Paris traceroute from RON and PlanetLab nodes to collect various datasets in 2007 and 2009. Our results show that the traditional concept of a single network path between hosts no longer holds. For instance, 39% of the source-destination pairs in our 2007 traces traverse a load balancer. This fraction increases to 72% if we consider the paths between a source and a destination network. In 2009, we notice a consolidation of per-flow and per-destination techniques and confirm that per-packet load balancing is rare.

Designing a QoS-aware, yet energy-saving routing protocol for WSNs is a notoriously hard problem. However, the outstanding interest for this technology, and the growing number of envisioned applications, motivate the need to introduce the notion of Quality of Service (QoS) in these networks. This paper introduces EDEAR (Energy and Delay Efficient Adaptive Routing), an adaptive routing algorithm based on route exploration and reinforcement learning. We evaluate EDEAR with simulations, under various network mobility conditions. Our results show that EDEAR outperforms any other routing protocol, delivering packets with the shortest delay, while reducing energy consumption. As a result, EDEAR's features allow to increase the network lifetime by 9-18%.

Internet exchange points (IXPs) are an important ingredient of the Internet AS-level ecosystem - a logical fabric of the Internet made up of about 30,000 ASes and their mutual business relationships whose primary purpose is to control and manage the flow of traffic. Despite the IXPs' critical role in this fabric, little is known about them in terms of their peering matrices (i.e., who peers with whom at which IXP) and corresponding traffic matrices (i.e., how much traffic do the different ASes that peer at an IXP exchange with one another). In this paper, we report on an Internet-wide traceroute study that was specifically designed to shed light on the unknown IXP-specific peering matrices and involves targeted traceroutes from publicly available and geographically dispersed vantage points. Based on our method, we were able to discover and validate the existence of about 44K IXP-specific peering links - nearly 18K more links than were previously known. In the process, we also classified all known IXPs depending on the type of information required to detect them. Moreover, in view of the currently used inferred AS-level maps of the Internet that are known to miss a significant portion of the actual AS relationships of the peer-to-peer type, our study provides a new method for augmenting these maps with IXP-related peering links in a systematic and informed manner.

Traceroute is widely used to report the path packets take between two Internet hosts, but the widespread deployment of load balancing routers breaks a basic assumption - that there is only a single such path. We specify an adaptive, stochastic probing algorithm, the multipath detection algorithm (MDA), to report all paths that probes can follow between a source and a destination. We establish the foundations of, and show how to calculate, rigorous statistical guarantees for the discovery of the entire multipath route. We explore algorithm cost/guarantee tradeoffs in real experiments and show the inadequacy of the classic practice of sending three probes per hop.

Tools to measure internet properties usually assume the existence of a single path from a source to a destination. However, load-balancing capabilities, which create multiple active paths between two end-hosts, are available in most contemporary routers. This paper proposes a methodology to identify load-balancing routers and characterize load-balanced paths. We enhance our traceroute-like tool, called Paris traceroute, to find all paths between a pair of hosts, and use it from 15 sources to over 68 thousand destinations. Our results show that the traditional concept of a single network path between hosts no longer holds. For instance, 39% of the source-destination pairs in our traces traverse a load balancer. Furthermore, this fraction increases to 70% if we consider the paths between a source and a destination network.

Traceroute [2] is used to learn the path between two machines in the internet. Uses range from the diagnosis of network problems to the assemblage of internet maps. Unfortunately, traceroute measurements can be inaccurate and incomplete when the measured route traverses a load balancer.

Traceroute is widely used, from the diagnosis of network problems to the assemblage of internet maps. However, there are a few serious problems with this tool, in particular due to the presence of load balancing routers in the network. This paper describes a number of anomalies that arise in nearly all traceroute-based measurements. We categorize them as "loops", "cycles", and "diamonds". We provide a new publicly-available traceroute, called Paris traceroute, which controls packet header contents to obtain a more precise picture of the actual routes that packets follow. This new tool allows us to find conclusive explanations for some of the anomalies, and to suggest possible causes for others.