Networking/ESnet related efforts|
If questions, send an email to Alex Sim (asim at lbl dot gov)
- J. Kim, A. Sim, J. Kim, K. Wu, "Botnets Detection Using Recurrent Variational Autoencoder", IEEE Global Communications Conference (Globecom 2020), 2020.
- G. R. Ghosal, D. Ghosal, A. Sim, A. V. Thakur, K. Wu, "A Deep Deterministic Policy Gradient Based Network Scheduler For Deadline-Driven Data Transfers", International Federation for Information Processing (IFIP) Networking Conference (NETWORKING 2020), 2020.
- J. Kim, A. Sim, J. Kim, K. Wu, J. Hahm, "Transfer Learning Approach for Botnet Detection based on Recurrent Variational Autoencoder", ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2020.
- S. Bhandari, A. K. Kukreja, A. Lazar, A. Sim, K. Wu, "Feature Selection and Tree-based Classification for Wireless Intrusion Detection", ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2020.
- M. Nakashima, A. Sim, J. Kim, "Evaluation of Deep Learning Models for Network PerformancePrediction for Scientific Facilities", ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2020.
- I. Monga, C. Guok, J. MacAuley, A. Sim, H. Newman, J. Balcas, P. DeMar, L. Winkler, T. Lehman, X. Yang, "SDN for End-to-end Networked Science at the Exascale", Future Generation Computer Systems, Elsevier, 2020, doi:10.1016/j.future.2020.04.018.
- D. Ghosal, S. Shukla, A. Sim, A. V. Thakur, K. Wu, "A Reinforcement Learning Based Network Scheduler For Deadline-Driven Data Transfers", IEEE Global Communications Conference (GLOBECOM 2019), 2019. doi:10.1109/GLOBECOM38437.2019.9013255
- J. Kim, A. Sim, B. Tierney, S. Suh, I. Kim, "Multivariate Network Traffic Analysis using Clustered Patterns", Journal of Computing, Springer, Vol. 101, No. 4, pp. 339-361, April 2019. doi:10.1007/s00607-018-0619-4.
- J. Kim, A. Sim, "A New Approach to Online, Multivariate Network Traffic Analysis", Journal of Computer Science and Technology, Special Section on Computer Networks and Distributed Computing, Vol. 34, No. 2, pp. 388–402, 2019. doi:10.1007/s11390-019-1915-y
- A. Syal, A. Lazar, J. Kim, K. Wu, A. Sim, "Automatic Detection of Network Traffic Anomalies and Changes", the 2nd International Workshop on Systems and Network Telemetry and Analytics (SNTA 2019), in conjunction with ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2019), 2019. doi:10.1145/3322798.3329255
- I. Monga, C. Guok, J. MacAuley, A. Sim, H. Newman, J. Balcas, P. DeMar, L. Winkler, T. Lehman, X. Yang, "SDN for End-to-end Networked Science at the Exascale (SENSE)", Innovate the Network for Data-Intensive Science Workshop (INDIS 2018), in conjunction with the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'18), 2018. doi:10.1109/INDIS.2018.00007
- C. Dao, X. Liu, J. Jiang, A. Sim, C. E. Tull, K. Wu, "Modeling Data Transfers: Change Point and Anomaly Detection", International Workshop on Scalable Network Traffic Analytics (SNTA 2018), 2018, in conjunction with the 38th IEEE International Conference on Distributed Computing Systems (ICDCS 2018). doi:10.1109/icdcs.2018.00177
- R. Kettimuthu, Z. Liu, I. Foster, P. Beckman, A. Sim, K. Wu, W. Liao, Q. Kang, A. Agrawal, A. Choudhary, "Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues", Workshop in Autonomous Infrastructure for Science (AI-Science 2018), 2018, in conjunction with the 27th International Symposium on High-Performance Parallel and Distributed Computing (ACM HPDC 2018). doi:10.1145/3217197.3217205.
- J. Kim, A. Sim, "A New Approach to Online, Multivariate Network Traffic Analysis", 2nd Workshop on Network Security Analytics and Automation (NSAA), in conjunction with the 26th International Conference on Computer Communications and Networks (ICCCN 2017), 2017
- J. Kim, A. Sim, S.C. Suh, I. Kim, "An Approach to Online Network Monitoring Using Clustered Patterns", International Conference on Computing, Networking and Communications (ICNC 2017), 2017.
- J. Kim, W. Yoo, A. Sim, S.C. Suh, I. Kim, "A Lightweight Network Anomaly Detection Technique", International Workshop on Computing, Networking and Communications (CNC 2017), 2017.
- W. Yoo, A. Sim, "Time-series Forecast Modeling on High-Bandwidth Wide Area Network Measurements", Journal of Grid Computing, Vol. 14, Issue 3, pp 463-476, doi:10.1007/s10723-016-9368-9, September 2016. http://link.springer.com/article/10.1007/s10723-016-9368-9.
- S. Shannigrahi, A. J. Barczyk, C. Papadopoulos, A. Sim, I. Monga, H. Newman, K. Wu, E. Yeh, "Named Data Networking in Climate Research and HEP Applications", the 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015), 2015
- K. Hu, J. Choi, A. Sim, J. Jiang, "Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis", International Journal of Statistics and Probability, Vol. 4, No. 2, p132-148, ISSN 1927-7032, 2015, doi:10.5539/ijsp.v4n2p132
- W. Yoo, A. Sim, "Network Bandwidth Utilization Forecast Model on High Bandwidth Networks", IEEE International Conference on Computing, Networking and Communications (ICNC'15), 2015
- W. Yoo, A. Sim, "Efficient Changing Pattern Detection on High Bandwidth Network Measurements", 7th International Conference on Grid and Distributed Computing (GDC 2014), 2014
- A. L. Chervenak, A. Sim, J. Gu, R. Schuler, N. Hirpathak, "Adaptation and Policy-Based Resource Allocation for Efficient Bulk Data Transfers in High Performance Computing Environments", 4th International Workshop on Network-aware Data Management (NDM'14), 2014
- A. L. Chervenak, A. Sim, J. Gu, R. Schuler, N. Hirpathak, "Efficient Data Staging Using Performance-Based Adaptation and Policy-Based Resource Allocation", the 22nd Euromicro International Conference on Parallel, Distributed and Network-based Processing, 2014
- K. Hu, A. Sim, D. Antoniades, C. Dovrolis, "Estimating and Forecasting Network Traffic Performance based on Statistical Patterns Observed in SNMP data", the 9th International Conference on Machine Learning and Data Mining (MLDM), 2013
- J. Gu, D. Smith, A. L. Chervenak, A. Sim, "Adaptive Data Transfers that Utilize Policies for Resource Sharing", the 2nd International Workshop on Network-Aware Data Management Workshop (NDM2012), 2012
- M. Balman, E. Pouyoul, Y. Yao, E. W. Bethel, B. Loring, Prabhat, J. Shalf, A. Sim, B. L. Tierney, "Experiences with 100Gbps Network Applications", the Fifth International Workshop on Data Intensive Distributed Computing (DIDC 2012), 2012
- Junmin Gu, Dimitrios Katramatos, Xin Liu, Vijaya Natarajan, Arie Shoshani, Alex Sim, Dantong Yu, Scott Bradley, Shawn McKee, "StorNet: Co-Scheduling of End-to-End Bandwidth Reservation on Storage and Network Systems for High Performance Data Transfers", Proceedings of IEEE INFOCOM HSN 2011, Shanghai China, 2011
- Mehmet Balman, Evangelos Chaniotakis, Arie Shoshani, Alex Sim, "A Flexible Reservation Algorithm for Advance Network Provisioning", Supercomputing 2010, 2010
- A. Sim, M. Balman, D. Williams, A. Shoshani, V. Natarajan, "Adaptive Transfer Adjustment in Efficient Bulk Data Transfer Management for Climate Datasets", the 22nd International Conference on Parallel and Distributed Computing and Systems (PDCS 2010), 2010
- Junmin Gu, Dimitrios Katramatos, Xin Liu, Vijaya Natarajan, Arie Shoshani, Alex Sim, Dantong Yu, Scott Bradley, Shawn McKee, "StorNet: Integrated Dynamic Storage and Network Resource Provisioning and Management for Automated Data Transfers", the 18th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2010), Taipei Taiwan, 2010
- A. Sim, D. Gunter, V. Natarajan, A. Shoshani, D. Williams, J. Long, J. Hick, J. Lee, E. Dart, "Efficient Bulk Data Replication for the Earth System Grid", International Symposium on Grid Computing, Data Driven e-Science: Use Cases and Successful Applications of Distributed Computing Infrastructures (ISGC 2010), 2010
- R. Kettimuthu, A. Sim, D. Gunter, B. Allcock, P. Bremer, J. Bresnahan, A. Cherry, L. Childers, E. Dart, I. Foster, K. Harms, J. Hick, J. Lee, M. Link, J. Long, K. Miller, V. Natarajan, V. Pascucci, K. Raffenetti, D. Ressman, D. Williams, L. Wilson, L. Winkler, "Lessons learned from moving Earth System Grid data sets over a 20 Gbps wide-area network", the 19th ACM International Symposium on High Performance Distributed Computing (HPDC), 2010
- Xrootd and Xcache, Juy 30, 2020
Dr. Wei Yang, at SLAC
Slides (PDF), Talks (YouTube, 54:49)
Xcache is a data caching software originated from the Xrootd - a popular storage system from the high energy physics community. Over the last several years, Xcache has generated wide interests in that community. By using multiple threads, memory caching, multiple IO queues, and asynchronized network and disk IO, the system efficiently utilizes the hardware capabilities. By caching file segments rather than whole files, it works well for both small and large data files. Xcache is also highly customizable to adapt different use case scenarios. In this talk, we will go over some of the use cases of Xcache in HEP using the xroot protocol, including a deployment tailored for the NERSC environment, and a prototype of Xcache that uses HTTP protocol as a general propose cache, as well as with HTTPs protocol as application specific cache.
Dr. Wei Yang is an Information Systems Specialist at the SLAC National Accelerator Lab. He obtained his Ph.D in experimental high energy physics from Colorado State University in 2000, and joined SLAC's computing division in September 2000. Wei started working on the Grid computing since 2005, and led and built the ATLAS experiment's Western Tier 2 facility since 2006. Working with SLAC and LBNL, Wei brought Xrootd storage system to ATLAS and Grid computing in 2008, and has since worked on various Xrootd projects such as Federated ATLAS Xrootd (FAX), Xcache, Third Party Copy with Xrootd. Wei also has broad interests in other aspects on scientific computing, and has worked on using fat containers on HPC sites (to enhance IO performance and distribute ATLAS software), porting ATLAS event service to Cori and Edison, developing Jupyter environment for ATLAS user analysis, etc.
- Data use by the CMS experiment at the LHC, May 15, 2020
Prof. Frank Würthwein, physics professor at UC San Diego
Slides (PDF), Talks (YouTube, 1:16:15)
Note: Presentation recording had audio issues for the first 4.5 slides.
ATLAS and CMS, the two large multi-purpose experiments at the LHC expect to each
be producing roughly one exabyte of new data per year
during the HL-LHC era, starting towards the end of this decade.
The US has traditionally been responsible for archival, processing,
and analysis of roughly 30-40% of the data of these two experiments.
We will describe data use by CMS at the Large Hadron Collider starting from first
principles, i.e. how the physics being done leads to data formats, data access,
and thus ultimately the directions R&D is being taken to make deriving science
from the large data volumes of the HL-LHC affordable operationally.
We will pick examples from CMS to be explicit,
but much of what will be presented applies also to ATLAS, at least in principle.
Frank Würthwein is the Executive Director of the Open Science Grid,
a national cyberinfrastructure to advance the sharing of resources, software,
and knowledge, and a physics professor at UC San Diego. He received his Ph.D.
from Cornell in 1995. After holding appointments at Caltech and MIT,
he joined the UC San Diego faculty in 2003.
His research focuses on experimental particle physics and
distributed high-throughput computing. His primary physics interests lie in
searching for new phenomena at the high energy frontier with the CMS detector
at the Large Hadron Collider. His topics of interest include,
but are not limited to, the search for dark matter, supersymmetry, and
electroweak symmetry breaking. As an experimentalist,
he is interested in instrumentation and data analysis.
In the last few years, this meant developing, deploying, and
now operating a worldwide distributed computing system for high-throughput
computing with large data volumes.
In 2010, "large" data volumes are measured in Petabytes. By 2030, they are expected to have grown to Exabytes.