Brice Augustin

Multipath tracing with Paris traceroute

Traceroute is a tool to report the route packets lake between two internet hosts. However, with the deployment of load balancing, there is no longer a single route to a destination, hence classic traceroute systematically misses some of these paths. In this paper, we specify an adaptive, stochastic probing algorithm, called the Multipath detection algorithm, to report all paths towards a destination. We have deployed this algorithm, probing from a single source towards multiple destinations. In our results, we have found Instances of load balancing with as many as 16 interfaces per hop. The algorithm also allows us to count load balancing routers, identify their locations, and characterize them by type.

Failure Control in Multipath Route Tracing

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.

QoE/QoS-aware LTE downlink scheduler for VoIP with power saving

The emerging and fastest growing multimedia services such as Skype, GTalk and interactive video gaming have created new challenges for wireless communication technologies, especially in terms of resource allocation and power optimization of User Equipments (UEs). The efficient resources and the power optimization are very important in the next communication systems because new multimedia services are more resource and power hungry. Having more traffic flow in the downlink as compared to the uplink, the resource allocation schemes in the downlink are more important than those in the uplink. In this paper, we have proposed a new downlink scheduling scheme, known as Quality-aware DRX (Q-DRX) Scheme, for Long Term Evaluation (LTE) wireless networks that not only improves the Quality of Service (QoS) to satisfy the user?s experience of multimedia services but also saves the UE?s power by using the Discontinuous Reception (DRX). This paper investigates how the different durations of DRX Light and Deep Sleep cycles affect the QoS and influence the Quality of Experience (QoE) of end users, in the context of Voice over IP (VoIP) traffic delivery over the LTE wireless network. The performance of the Q-DRX scheme is evaluated with traditional scheduling schemes, in the perception of the following important parameters, System Throughput, Throughput Fairness Index, Packet Loss Rate and Packet Delay. Our proposed Q-DRX Scheme successfully minimized the packet?s delay and packet?s loss by considering the key parameters of QoS with fair resources distribution among the UEs to achieve a high satisfaction level.

On Traffic Patterns of HTTP Applications

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.

QoE-based LTE downlink scheduler for VoIP

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.

QoE Power-Efficient Multimedia Delivery Method for LTE-A

The fastest growing of multimedia services over future wireless communication systems demands more network resources, efficient delivery of multimedia service with high user satisfaction, and power optimization of user equipments (UEs). The resources and power optimization are significant in future mobile computing systems because emerging multimedia services consume more resources and power. The 4G standard of Long Term Evolution-Advanced (LTE-A) wireless system has adopted the discontinuous reception (DRX) method to extend and optimize the UE battery life, while there is no standard scheduling method to distribute the radio resources among the UE. This paper presents a downlink scheduler, i.e., Quality of Experience Power Efficient Method (QEPEM) for LTE-A, which efficiently allocates the radio resources and optimizes the use of UE power using the DRX mechanism. We investigate how the different duration of DRX Light and Deep Sleep cycle influences the Quality of Service (QoS) and QoE of end users, using voice over IP (VoIP) over the LTE-A. The QEPEM is evaluated with the traditional methods, in terms of system throughput, fairness index, packet loss rate, and packet delay. The QEPEM measures the users QoE and feeds back to the eNodeB for scheduling decisions along with other important parameters. Our proposed method reduces the packet delay and packet loss and increases the fairness and UE power saving with high user satisfaction.

QoE in 5G cloud networks using multimedia services

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 users 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 users QoE.

Swarm intelligence routing approach in networked robots

Robot swarm combined with wireless communication has been a key driving force in recent few years and has currently expanded to wireless multihop networks, which include ad hoc radio networks, sensor networks, wireless mesh networks, etc. The aim of this paper is to propose an approach which introduces a polynomial time approximation path navigation algorithm and constructs dynamic state-dependent navigation policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize simultaneously two criteria: cumulative cost path and end-to-end delay path. The approach samples, estimates, and builds the model of pertinent aspects of the environment. It uses a model that combines both a stochastic planned prenavigation for the exploration phase and a deterministic approach for the backward phase. To show the robustness and performances of the proposed approach, simulation scenario is built through the specification of the interested network topology and involved network traffic between robots. For this, this approach has been compared to traditional optimal path routing policy.

Regulating QoE for adaptive video streaming using BBF method

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 users 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 users device properties. The proposed method considers three important Quality of Service (QoS) factors that regulate the users 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 users QoE. The proposed BBF algorithm is evaluated with different buffer length, and it is compared to Adobes OSMF adaptive method.

Crowd-sourcing framework to assess QoE

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 users 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-users system. The proposed approach provides the opportunity to explore the users quality perception in a wider domain.