http://hdl.handle.net/10468/227 2017-10-29T23:47:40Z 2017-10-29T23:47:40Z Raca, Darijo Zahran, Ahmed H. Sreenan, Cormac J. Sinha, Rakesh K. Halepovic, Emir Jana, Rittwik Gopalakrishnan, Vijay http://hdl.handle.net/10468/4934 2017-10-27T11:00:41Z 2017-10-16T00:00:00Z

Back to the future: Throughput prediction for cellular networks using radio KPIs Raca, Darijo; Zahran, Ahmed H.; Sreenan, Cormac J.; Sinha, Rakesh K.; Halepovic, Emir; Jana, Rittwik; Gopalakrishnan, Vijay The availability of reliable predictions for cellular throughput would offer a fundamental change in the way applications are designed and operated. Numerous cellular applications, including video streaming and VoIP, embed logic that attempts to estimate achievable throughput and adapt their behaviour accordingly. We believe that providing applications with reliable predictions several seconds into the future would enable profoundly better adaptation decisions and dramatically benefit demanding applications like mobile virtual and augmented reality. The question we pose and seek to address is whether such reliable predictions are possible. We conduct a preliminary study of throughput prediction in a cellular environment using statistical machine learning techniques. An accurate prediction can be very challenging in large scale cellular environments because they are characterized by highly fluctuating channel conditions. Using simulations and real-world experiments, we study how prediction error varies as a function of prediction horizon, and granularity of available data. In particular, our simulation experiments show that the prediction error for mobile devices can be reduced significantly by combining measurements from the network with measurements from the end device. Our results indicate that it is possible to accurately predict achievable throughput up to 8 sec in the future where 50th percentile of all errors are less than 15% for mobile and 2% for static devices.

2017-10-16T00:00:00Z Zahran, Ahmed H. Quinlan, Jason J. Ramakrishnan, K. K. Sreenan, Cormac J. http://hdl.handle.net/10468/4924 2017-10-27T09:05:21Z 2017-06-01T00:00:00Z

SAP: Stall-aware pacing for improved DASH video experience in cellular networks Zahran, Ahmed H.; Quinlan, Jason J.; Ramakrishnan, K. K.; Sreenan, Cormac J. The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric.

2017-06-01T00:00:00Z Young, Gareth W. Murphy, David Weeter, Jeffrey http://hdl.handle.net/10468/4094 2017-06-20T11:00:54Z 2017-05-19T00:00:00Z

A qualitative analysis of haptic feedback in music focused exercises new interfaces for musical expression Young, Gareth W.; Murphy, David; Weeter, Jeffrey We present the findings of a pilot-study that analysed the role of haptic feedback in a musical context. To closely examine the role of haptics in Digital Musical Instrument (DMI) design an experiment was formulated to measure the users’ perception of device usability across four separate feedback stages: fully haptic (force and tactile combined), constant force only, vibrotactile only, and no feedback. The study was piloted over extended periods with the intention of exploring the application and integration of DMIs in real-world musical contexts. Applying a music orientated analysis of this type enabled the investigative process to not only take place over a comprehensive period, but allowed for the exploration of DMI integration in everyday compositional and explorative practices. As with any investigation that involves creativity, it was important that the participants did not feel rushed or restricted. That is, they were given sufficient time to explore and assess the different feedback types without constraint. This provided an accurate and representational set of qualitative data for validating the participants’ experience with the different feedback types they were presented with.

2017-05-19T00:00:00Z Khalid, Ahmed Quinlan, Jason J. Sreenan, Cormac J. http://hdl.handle.net/10468/4757 2017-09-21T11:00:54Z 2017-03-07T00:00:00Z

MiniNAM: A network animator for visualizing real-time packet flows in Mininet Khalid, Ahmed; Quinlan, Jason J.; Sreenan, Cormac J. In this demonstration we present MiniNAM, a utility that provides real-time animation of networks created by the Mininet emulator. Mininet is one of the most well-known network emulators in research and academia. Although Mininet is capable of emulating both traditional and software-defined networks, it does not provide a tool to visually observe and monitor the packets flowing over the created network topology. Our utility includes all the components required to initiate, visualize and modify Mininet network flows in real-time. MiniNAM provides a graphical user interface that allows dynamic modification of preferences and packet filters: a user can view selective flows with options to color code packets based on packet type and/or source node. This establishes MiniNAM as a very powerful tool for debugging network protocols or teaching, learning and understanding network concepts. This demonstration illustrates a number of sample use cases and examples of using MiniNAM to create networks and view the generated network flows with customized preferences.

2017-03-07T00:00:00Z