International Workshop on Scalable Network Traffic Analytics (SNTA'18)
CFP: https://sntaworkshop.github.io/2018/SNTA2018-CFP.html
CFP: https://easychair.org/cfp/SNTA2018
Program: https://sdm.lbl.gov/snta/2018
SNTA archive
Email: SNTA.help@gmail.com
In conjunction with ICDCS2018 Workshop Program
Session 1 Keynote Presentation Session 2
A Computation Workload Characteristic Study of C-RAN (slides) An Empirical
Study on Network Anomaly Detection using Convolutional Neural Networks (slides)
Spatio-temporal
Analysis of HPC I/O and Connection Data (slides) Session 3
A comprehensive study of wide area data movement at a scientific computing facility (slides) Modeling Data
Transfers: Change Point and Anomaly Detection (slides) Website
Fingerprinting Attack Mitigation using Traffic Morphing (slides) Realistic Cover Traffic to
Mitigate Website Fingerprinting Attacks (slides)
July 2-5, 2018
Vienna, Austria
http://icdcs2018.ocg.at/
2 July 2018
1:30pm - 5:30pm (local time)
Program subject to change
(date/time confirmed)
1:30pm - 2:20pm
Chair Jinoh Kim
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
2:30pm - 3:35pm
Chair Jerry Chou
(122)
Yu-Cing Luo, Shih-Chun Huang, Bing-Liang Chen, Jerry Chou
(National Tsing Hua University)
Donghwoon Kwon, Kathiravan Natarajan, Sang C. Suh, Jinoh Kim
(Texas A&M University, Commerce)
Hyunjoo Kim (ETRI)
Jinoh Kim, Jinhwan Choi (Texas A&M University, Commerce)
Alex Sim (Lawrence Berkeley National Laboratory)
Break
4:00pm - 5:35pm
Chair Eric Chan-Tin
(1121)
Zhengchun Liu, Rajkumar Kettimuthu, Ian Foster (University of
Chicago)
Yuanlai Liu (University of California, Riverside)
Cecilia Dao (Yale University)
Xinyu Liu (University of California, Berkeley)
Alex Sim, Craig
E. Tull, Kesheng Wu (Lawrence Berkeley National Laboratory)
Eric Chan-Tin (Oklahoma State University)
Taejoon Kim, Jinoh Kim (Texas A&M University, Commerce)
Weiqi Cui, Jiangmin Yu,
Yanmin Gong, Eric Chan-Tin (Oklahoma State University)