Performance evaluation and benchmarking of the JXTA peer-to-peer platform

Peer-to-peer (P2P) systems are a relatively new addition to the large area of distributed computer systems. The emphasis on sharing resources, self-organization and use of discovery mechanisms sets the P2P systems apart from other forms of distributed computing. Project JXTA is the first P2P application development platform, consisting of standard protocols, programming tools and multi-language implementations. A JXTA peer network is a complex overlay, constructed on top of the physical network, with its own identification scheme and routing. This thesis investigates the performance of JXTA using benchmarking. The presented work includes the development of the JXTA Performance Model and Benchmark Suite, as well as the collection and analysis of the performance results. By evaluating three major versions of the protocol implementations in a variety of configurations, the performance characteristics, limitations, bottlenecks and trade-offs are observed and discussed. It is shown that the complexity of JXTA allows many factors to affect its performance and that several JXTA components exhibit unintuitive and unexpected behavior. However, the results also reveal the ways to maximize the performance of the deployed and newly designed systems. The evolution of JXTA through several versions shows some notable improvements, especially in search and discovery models and added messaging components, which make JXTA a promising member of the future generation of computer systems

Back to the future: Throughput prediction for cellular networks using radio KPIs

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

OSCAR: an optimized stall-cautious adaptive bitrate streaming algorithm for mobile networks

The design of an adaptive video client for mobile users is challenged by the frequent changes in operating conditions. Such conditions present a seemingly insurmountable challenge to adaptation algorithms, which may fail to find a balance between video rate, stalls, and rate-switching. In an effort to achieve the ideal balance, we design OSCAR, a novel adaptive streaming algorithm whose adaptation decisions are optimized to avoid stalls while maintaining high video quality. Our performance evaluation, using real video and channel traces from both 3G and 4G networks, shows that OSCAR achieves the highest percentage of stall-free sessions while maintaining a high quality video in comparison to the state-of-the-art algorithms

Incorporating prediction into adaptive streaming algorithms: a QoE perspective

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error. We find that by applying accurate prediction to three algorithms, user QoE can improve up to 55% depending on the algorithm in use. Furthermore having longer horizon positively affects QoE metrics. Accurate predictions have the most significant impact on stall performance by completely eliminating them. Prediction also improves switching behaviour significantly and longer prediction horizons enable a client to promptly reduce quality and avoid stalls when the throughput drops for a relatively long time that can deplete the buffer. For all algorithms, a 3-chunk horizon strikes the best balance between different QoE metrics and, as a result, achieving highest user QoE. While error-induced predictions significantly lower user QoE in certain situations, on average, they provide 15% improvement over DASH algorithms without any prediction

Broadband wireless access: perspectives and performance

Bibliography: p. 153-163A few pages are in colour

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may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying, publication, or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis

Characterizing and modeling user mobility in a cellular data network

The demand for cellular data networks is expected to increase with 3G and beyond technologies accompanied by highbandwidth consumer services, such as wireless video and camera phones. User mobility affects quality of service, and makes capacity planning more difficult. This paper presents an analysis of user mobility patterns based on data traffic traces from a major regional CDMA2000 cellular network. We find low overall mobility in the network, power-law characteristics in user mobility profiles, and weak correlations between call activity and mobility levels for individual users. We also find that users concentrate their activity in a “home cell ” with frequent shorter trips to other locations in the network. Based on the empirical findings, we develop and parameterize a model of cellular data user mobility and show its practical use in simulation

ABSTRACT Characterizing and Modeling User Mobility in a Cellular Data Network

The demand for cellular data networks is expected to increase with 3G and beyond technologies accompanied by highbandwidth consumer services, such as wireless video and camera phones. User mobility affects quality of service, and makes capacity planning more difficult. This paper presents an analysis of user mobility patterns based on data traffic traces from a major regional CDMA2000 cellular network. We find low overall mobility in the network, power-law characteristics in user mobility profiles, and weak correlations between call activity and mobility levels for individual users. We also find that users concentrate their activity in a “home cell ” with frequent shorter trips to other locations in the network. Based on the empirical findings, we develop and parameterize a model of cellular data user mobility and show its practical use in simulation