godash 2.0 - The Next Evolution of HAS Evaluation

godash 2.0 - The Next Evolution of HAS Evaluation O'Sullivan, John; Raca, Darijo; Quinlan, Jason J. In this short demo paper, we introduce godash 2.0 godash is a headless HTTP adaptive streaming (HAS) video streaming platform written in the Google programming language GO. godash has been extensively rewritten for this release so as to provide ease of use, and a host of new features. godash includes options for eight different state of the art adaptive algorithms, five HAS profiles, four video codecs, the ability to stream audio and video segments, two transport protocols, real-time output from five Quality of Experience (QoE) models, as well as a collaborative framework for the evaluation of cooperative HAS streaming. godash also comes complete with its own testbed framework known as godashbed. godashbed uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. godashbed has options for large scale evaluation of HAS streaming using 4G/5G bandwidth traces, various modes of background traffic, and a choice of web server, namely: Web Server Gateway Interface (WSGI) and Asynchronous Server Gateway Interface (ASGI). In this manner, godash provides a framework for rapid deployment and testing of new HAS algorithms, QoE models and transport protocols.
http://hdl.handle.net/10468/10145

Gender differences in personality traits of software engineers

Gender differences in personality traits of software engineers Russo, Daniel; Stol, Klaas-Jan There is a growing body of gender studies in software engineering to understand diversity and inclusion issues, as diversity is recognized to be a key issue to healthy teams and communities. A second factor often linked to team performance is personality, which has received far more attention. Very few studies, however, have focused on the intersection of these two fields. Hence, we set out to study gender differences in personality traits of software engineers. Through a survey study we collected personality data, using the HEXACO model, of 483 software engineers. The data were analyzed using a Bayesian independent sample t-test and network analysis. The results suggest that women score significantly higher in Openness to Experience, Honesty-Humility, and Emotionality than men. Further, men show higher psychopathic traits than women. Based on these findings, we develop a number of propositions that can guide future research.
http://hdl.handle.net/10468/10183

Beyond throughput: the next Generation a 5G dataset with channel and context metrics

Beyond throughput: the next Generation a 5G dataset with channel and context metrics Raca, Darijo; Leahy, Dylan; Sreenan, Cormac J.; Quinlan, Jason J. In this paper, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns (video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our realtime 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. Our framework, permits other researchers to investigate this interaction through the generation of their own synthetic datasets.
http://hdl.handle.net/10468/9826

goDASH - GO accelerated HAS framework for rapid prototyping

goDASH - GO accelerated HAS framework for rapid prototyping Raca, Darijo; Manifacier, Maëlle; Quinlan, Jason J. In this short paper, we present goDASH, an infrastructure for headless streaming of HTTP adaptive streaming (HAS) video content, implemented in the language golang, an open-source programming language supported by Google. goDASHâ s main functionality is the ability to stream HAS content without decoding actual video (headless player). This results in low memory requirements and the ability to run multiple players in a large-scale-based evaluation setup. goDASH comes complete with numerous state-of-the-art HAS algorithms, and is fully written in the Google golang language, which simplifies the implementation of new adaptation algorithms and functions. goDASH supports two transportation protocols Transmission Control Protocol (TCP) and Quick UDP Internet Connections (QUIC). The QUIC protocol is a relatively new protocol with the promise of performance improvement over the widely used TCP. We believe that goDASH is the first emulation-based HAS player that supports QUIC. The main limitation in using QUIC protocol is the need for a security certificate setup on both ends (client and server) as QUIC demands an encrypted connection. This limitation is eased by providing our own testbed framework, known as goDASHbed. This framework uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. As part of Mininet, goDASH can be used in conjunction with other traffic generators.
http://hdl.handle.net/10468/9845