International Workshop on Scalable Network Traffic Analytics (SNTA'18)

CFP: https://sdm.lbl.gov/snta/2018/SNTA2018-CFP.html
CFP: https://easychair.org/cfp/SNTA2018
Program: https://sdm.lbl.gov/snta/2018
Email: SNTA.help@gmail.com  

In conjunction with ICDCS2018
July 2-5, 2018
Vienna, Austria
http://icdcs2018.ocg.at/

Workshop Program (DRAFT)
2 July 2018
1:30pm - 5:30pm (local time)
Program subject to change
(date/time confirmed)


Session 1
1:30pm - 2:20pm

Chair Jinoh Kim

Keynote Presentation
    Title: Scientific Data Services Framework for Exascale Infrastructure (slides)
    Abstract: Scientific facilities, including light sources, electronic microscopes, and high-energy colliders produce a vast amounts of data. Thousands of different analysis tasks are performed on these datasets to generate scientific insight. Often I/O operations are the bottleneck in these analysis operations. This work address the I/O efficiency issue by developing techniques for common data access patterns, for deep storage hierarchies, and for massive parallelism. Additionally, we present a thorough theoretical analysis of the data access cost to exploit the structural locality, and select the best array partitioning strategy for a given operation. In a series of performance tests on large scientific datasets, we have observed that our framework outperforms Spark by as much as 2070X on the same tasks.

    Dr. John Wu is a Senior Computer Scientist at Lawrence Berkeley National Laboratory. He works actively on a number of topics in data management, data analysis, and high-performance computing.  His algorithmic research work includes statistical methods for feature extraction, indexing techniques for searching large datasets, and matrix based techniques for machine learning and scientific computing. He has authored and coauthored more than 100 technical publications, 11 of which have more than 100 citations each. He is the developer of a number of software packages, including, IDEALEM, SDS, FastBit and TRLan.  Among them, the FastBit software for indexing large datasets has earned an R&D 100 Award, and is used by many organizations.  For example, a German bioinformatics company uses FastBit to accelerate their molecular docking software by hundreds of times, and an US internet company uses it to sift through terabytes of advertisement related data daily.  A FastBit paper is collected among the 40 major works funded by DOE Office of Science, as a part of its 40th Anniversary celebration.

Break

Session 2
2:30pm - 3:35pm

Chair Jerry Chou




(122)

A Computation Workload Characteristic Study of C-RAN (slides)
    Yu-Cing Luo, Shih-Chun Huang, Bing-Liang Chen, Jerry Chou (National Tsing Hua University)

An Empirical Study on Network Anomaly Detection using Convolutional Neural Networks (slides)
    Donghwoon Kwon, Kathiravan Natarajan, Sang C. Suh, Jinoh Kim (Texas A&M University, Commerce)
    Hyunjoo Kim (ETRI)

Spatio-temporal Analysis of HPC I/O and Connection Data (slides)
    Jinoh Kim, Jinhwan Choi (Texas A&M University, Commerce)
    Alex Sim (Lawrence Berkeley National Laboratory)

Break

Session 3
4:00pm - 5:35pm

Chair Eric Chan-Tin









(1121)

A comprehensive study of wide area data movement at a scientific computing facility (slides)
    Zhengchun Liu, Rajkumar Kettimuthu, Ian Foster (University of Chicago)
    Yuanlai Liu (University of California, Riverside)

Modeling Data Transfers: Change Point and Anomaly Detection (slides)
    Cecilia Dao (Yale University)
    Xinyu Liu (University of California, Berkeley)
    Alex Sim, Craig E. Tull, Kesheng Wu (Lawrence Berkeley National Laboratory)

Website Fingerprinting Attack Mitigation using Traffic Morphing (slides)
    Eric Chan-Tin (Oklahoma State University)
    Taejoon Kim, Jinoh Kim (Texas A&M University, Commerce)

Realistic Cover Traffic to Mitigate Website Fingerprinting Attacks (slides)
    Weiqi Cui, Jiangmin Yu, Yanmin Gong, Eric Chan-Tin (Oklahoma State University)