Session 1
Chair: Alex Sim
10am EDT

Keynote Presentation
    Prof. Kurt Stockinger, Zurich University of Applied Sciences
    Title: INODE - Intelligence Open Data Exploration
(Slides), (Presentation)
    Abstract: In order to query a database effectively, one must typically master a special database language such as SQL or SPARQL. These query languages can be compared, for example, to Esperanto - a language that only some specialists know, but not the general public. However, if someone is not proficient in SQL or SPARQL, it is almost impossible to sift through the hidden information of databases. Since the 1970s, computer science research has been concerned with the development of natural language interfaces to databases to facilitate human-machine interaction. Early systems mostly used rule- or pattern-based approaches to solve the problem. However, in practice, these systems did not catch on due to low accuracy. However, recent advances in Big Data, Machine Learning and the rapid development of processor technologies, have led to promising solutions such as the voice assistants from Amazon, Apple or Google. First, we motivate the vision of INODE based on three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. Afterwards we describe the INODE architecture and give insights into developing such a system for accessing open data sets. Next, we demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we elaborate on the lessons learned when developing such a system.

Prof. Kurt Stockinger is Professor of Computer Science, Director of Studies in Data Science at Zurich University of Applied Sciences (ZHAW) and Deputy Head of the ZHAW Datalab. His research focuses on Data Science with emphasis on Big Data, Natural Language Query Processing, Query Optimization and Quantum Computing. Essentially, his research interests are at the intersection of databases, natural language processing and machine learning. He is also on the Advisory Board of Callista Group AG and the International AIQT Foundation. Previously Prof. Stockinger worked at Credit Suisse in Zurich, Switzerland, at Lawrence Berkeley National Laboratory in Berkeley, California, at California Institute of Technology, California as well as at CERN in Geneva, Switzerland. He holds a Ph.D. in computer science from CERN / University of Vienna.     

Session 2

Chair: Massimo Cafaro
11am EDT












(1122)

Evaluations of Network Performance Enhancement on Cloud-native Network Function
(Slides), (Presentation), (Session Presentation)
    Yong-Xuan Huang,Jerry Chou (National Tsing Hua University)

Analyzing Scientific Data Sharing Patterns for In-network Data Caching
(Slides), (Presentation), (Session Presentation)
    Elizabeth Copps (Middlebury College), Huiyi Zhang (Univeresity of California, Berkeley), Alex Sim, Kesheng Wu (Lawrence Berkeley National Laboratory), Inder Monga, Chin Guok (Energy Sciences Network), Frank Würthwein, Diego Davila, Edgar Fajardo (University of California, San Diego)

A Hybrid Virtual Network Function Placement Strategy for Maximizing the Profit of Network Service Deployment over Dynamic Workload
(Slides), (Presentation), (Session Presentation)
    Chi-Chen Yang, Jerry Chou (National Tsing Hua University)

Characterizing Resource Heterogeneity in Edge Devices for Deep Learning Inferences
(Slides), (Session Presentation)
    Jianwei Hao, Piyush Subedi, In Kee Kim, Lakshmish Ramaswamy (University of Georgia)

Session 3
Chair: Jinoh Kim
1pm EDT

Keynote Presentation II
    Prof. Jerry Chou, National Tsing Hua University
    Title: Recent Advances and Future Challenges for Network Function Virtualization Infrastructure
(Slides), (Presentation)
    Abstract: Today’s enterprise networks has revolutionized by the emerging technology called Network function virtualization (NFV), which is a type of data center network architecture proposed by the Eu- ropean Telecommunications Standards Institute (ETSI). NFV uses virtualization techniques to implement various Network Functions (NFs) like firewall, load balancer etc. from dedicated network de- vices to virtualized instances in commodity servers. This virtualized instance is called as Virtual Network Function (VNF). The purpose of VNF is to process NFs in order to accomplish a specific task. Traditionally these NFs were implemented on dedicated network equipment called middleboxes. Although these middleboxes are capable of processing heavy workloads, they are expensive, inflexi- ble and require experts to maintain them. Therefore, NFV has the potential to substitute these middleboxes with virtualized instances in cloud datacenters, and hence greatly reduces the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of networks by making it cheaper, flexible and scalable.
This talk will share the recent advances and future challenges on how to build the infrastructure for hosting and managing NFVs. In particularly, we will focus on two of the most important topics in this research direction. First is the VNF placement problem, which aims to find the best mapping decision between VNF instances and physical resources. It has significant impact to the network operation cost, and service quality, but it is also known to be a NP-hard problem. So the problem has been actively studied by the research community. The second topic is Cloud-native/Container Network Function (CNF), which aims to minimize the overhead of traditional virtualization technique for network function using container-based technologies. Hence, it has drawn growing inter- ests from industry to build the NFV infrastructure for CNF, but many new challenges remain to be addressed and studied.

Prof. Jerry Chou received M.S./B.S. degrees from Department of Computer Science at National Tsing Hua University(NTHU) in 2003/2004, and the Ph.D. degree from the Department of Computer Science and Engineering at University of California, San Diego (UCSD), USA in 2009. Prof. Chou joined the Department of Computer Science at National Tsing Hua University in 2011 as an Assistant Professor, and was promoted to an Associate Professor in 2016. Between 2010 and 2011, Prof. Chou was a member of scientific data management group in Lawrence Berkeley National Lab (LBNL). Prof. Chou’s research interests are in the broad area of distributed systems including high performance computing, cloud/edge com- puting, big data, storage systems, and resource or data management. Prof. Chou developed FastQuery, a parallel query and indexing systems for scientific data, and proposed various scheduling or resource provisioning algorithms for optimizing performance and energy efficiency of cloud and storage systems. Recently, he has extended his work to focus on some emerging topics, such as NFV infrastructure, ML systems, and GPU computing. His work has led dozens of publication in prestigious international journals and conferences, and served as committee members and organizers in various conferences. He also received a outstanding paper award from International Symposium on Cloud and Service Computing in 2017, and best paper award from International Conference on Cloud Computing and Services Science in 2021.     

Session 4

Chair: Jinoh Kim
2pm EDT








(122)

GPU-based Classification for Wireless Intrusion Detection
(Slides), (Session Presentation)
    Alina Lazar (Youngstown State University), Alex Sim, Kesheng Wu (Lawrence Berkeley National Laboratory)

Programmable Per-Packet Network Telemetry: From Wire to Kafka at Scale
(Slides), (Session Presentation)
    Zhang Liu, Bruce Mah, Yatish Kumar, Chin Guok, Richard Cziva (Energy Sciences Network (ESnet))

Access Patterns to Disk Cache for Large Scientific Archive
(Slides), (Session Presentation)
    Yumeng Wang (University of California, Berkeley), Kesheng Wu, Alex Sim (Lawrence Berkeley National Laboratory), Shinjae Yoo, Shigeki Misawa (Brookhaven National Laboratory)

Session 5: Social

3:10pm - 4pm EDT

Zoom info: https://lbnl.zoom.us/j/96087375265
Meeting ID: 960 8737 5265
Passcode: only available during the session