Author: liux

Invited Talk: High-Performance Modeling and Simulation

High-Performance Modeling and Simulation of Computer Networks

Universidade Federal de São Carlos (UFSCar)
São Carlos, Brazil
March 12, 2018

Abstract

Modeling and simulation (M&S) plays an important role in the design analysis and performance evaluation of complex systems. Many of these systems, such as computer networks, involve a large number of interrelated components and processes. Complex behaviors emerge as these components and processes inter-operate across multiple scales at various granularities. M&S must be able to provide sufficiently accurate results while coping with the scale and complexity. My talk will focus on some novel techniques in high-performance network modeling and simulation. One is hybrid network traffic modeling, which can offload the computationally intensive bulk traffic calculations to the background onto GPU, while leaving detailed simulation of network transactions in the foreground on CPU. The other is distributed network emulation with simulation symbiosis, which uses an abstract network model to coordinate distributed emulation instances with superimposed traffic model to represent large-scale network scenarios.

Information’17 Paper: Investigating the Statistical Distribution of Learning Coverage in MOOCs

Investigating the Statistical Distribution of Learning Coverage in MOOCs, Xiu Li, Chang Men, Zhihui Du, Jason Liu, Manli Li, and Xiaolei Zhang. Information 2017, 8(4), 153; doi:10.3390/info8040150 – 20 November 2017. [paper]

Abstract

Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC.

Bibtex

@Article{info8040150,
AUTHOR = {Li, Xiu and Men, Chang and Du, Zhihui and Liu, Jason and Li, Manli and Zhang, Xiaolei},
TITLE = {Investigating the Statistical Distribution of Learning Coverage in MOOCs},
JOURNAL = {Information},
VOLUME = {8},
YEAR = {2017},
NUMBER = {4},
ARTICLE NUMBER = {150},
URL = {http://www.mdpi.com/2078-2489/8/4/150},
ISSN = {2078-2489},
DOI = {10.3390/info8040150}
}

Invited Talk: Introducing FIU CAESCIR

Introducing FIU CAESCIR

2017 Annual CIRI PI Meeting
University of Illinois, Urbana-Champaign, IL, USA
October 19, 2017

Abstract

This talk gives an introduction to the Center for Advancing Education and Research on Critical Infrastructure Resilience (CAESCIR), a new project sponsored by the Department of Homeland Security (DHS) at the Florida International University (FIU).

Slides

Talk: Virtual Time Machine for Reproducibility

Virtual Time Machine for Large-Scale Reproducible Distributed Emulation

2017 GEFI Workshop
Rio de Janeiro, Brazil
October 26, 2017

Abstract

Cyber-infrastructure and meta-cloud testbeds, such as GENI, CloudLab, and Chameleon, are shared facilities that can be configured to provide a diverse and yet controllable environment for testing network protocols and distributed applications. Combined with emulation capabilities, these testbeds provide automated tools for allocating resources, instantiating applications, and collecting measurements. To facilitate reproducibility, they provide support for re-creating the execution environment between experiment runs. A major issue, however, with reproducibility on these systems is the lack of accurate control of time, especially when the experiment faces resource oversubscription. Virtual time management has been proposed for scheduling time dilated virtual machines to increase time fidelity. We hereby propose a unified resource and time scheme on cyber-infrastructure and meta-cloud testbeds to enable large-scale, high-capacity, high-fidelity, reproducible distributed emulation.

Slides