The Seventh International Workshop on Systems and Network Telemetry and Analytics (SNTA 2024) Pisa, Italy, June 3 - 4, 2024 in conjunction with ACM HPDC 2024 https://sntaworkshop.github.io/2024/ Submission link https://snta24.hotcrp.com/paper/new Submission deadline March 1, 2024 The tasks of systems and network telemetry are vital for effective operations and management of HPC and distributed computing systems by offering comprehensive measurement and analysis capabilities to provide visibility into what is occurring at any time. The tasks will be significantly complicated with the greater complexity of computing systems, increasing network speed, and the newly introduced mobile and IoT devices. Such changes will render the existing telemetry and analysis techniques outdated, and more scalable techniques may be in place for data-driven and deeper data analysis. In addition to the quantitative and qualitative challenges, data pressure in systems and networks also comes from various sources such as sensors, computing systems, networking and security devices, and other emerging computing elements speaking with different syntax and semantics, which makes organizing and incorporating the generated data difficult for extensive analysis. This workshop aims at bridging the systems and network measurement and the latest advances in artificial intelligence and data science technologies to advance the performance and reliability of HPC and distributed systems. New analysis techniques are needed in the modern world from the diverse angles of systems/network performance, availability, and security. For example, real-time streaming analytics algorithms and methods need to be explored for estimating network performance and summarizing the traffic variables to capture the network activities due to the network bandwidth increase. Multivariate analysis of telemetric variables may provide an intuitive, comprehensive view of the systems and network dynamics. New logging techniques are also needed with the latest developments in storage and archival technologies. In addition, many applications in this area may need to address the application-specific requirements and challenges. This workshop intends to share visions of investigating new approaches and methods at the intersection of data sciences and HPC/distributed computing systems. Topics of Interest (but not limited to) • Systems and network measurement, analysis, and summarization • Data-driven, multivariate, streaming-based data processing • Scalable distributed and federated machine learning • Performance modeling, analysis, and optimization • Cybersecurity, forensics, privacy, and anonymization • Intelligent workflow, visualization, and applications • Smart instruments, edge/fog-systems, and IoTs • Wireless, sensor, 5G/6G networks measurement and analysis • Software/Knowledge-Defined-* technologies • Advances in network and storage technologies • Design and evaluation of HPC and distributed systems • Best practices and implementations tied to systems/network data analysis Submission Guidelines All papers must be original and not simultaneously submitted to another journal or conference. Authors are invited to submit either a full (max 8 pages) paper or a short/work-in-progress (max 5 pages) paper. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library. Submission link:  https://snta24.hotcrp.com/paper/new Important Dates • Submission deadline: March 1, 2024 • Author notification: April 1, 2024 • Camera-ready deadline: April 18, 2024 • Workshop: June 3-4, 2024 Program Committee • Mohammed Abuhamad, Loyola University Chicago, USA • Stergios Anastasiadis, University of Ioannina, Greece • Engin Arslan, University of Nevada at Reno, USA • Sang-Yoon Chang, University of Colorado at Colorado Springs, USA • Ming-Hung Chen, IBM, USA • Chunglae Cho, ETRI, Korea • Italo Epicoco, University of Salento, Italy • Wenjun Fan, Xi'an Jiaotong-Liverpool University, China • In Kee Kim, University of Georgia, USA • Alina Lazar, Youngstown State University, USA • Chul-Ho Lee, Texas State University, USA • Dongeun Lee, Texas A&M University-Commerce, USA • Che-Rung Lee, National Tsing Hua University, Taiwan • Kwangsung Oh, University of Nebraska Omaha, USA • Amir H. Payberah, KTH Royal Institute of Technology, Sweden • Marco Pulimeno, University of Salento, Italy • Thomas Ropars, Univ. Grenoble Alpes, France • Alex Sim, Lawrence Berkeley National Laboratory, USA • Houjun Tang, Lawrence Berkeley National Laboratory, USA • K. John Wu, Lawrence Berkeley National Laboratory, USA Organizing Committee • Massimo Cafaro, University of Salento, Italy • Eric Chan-Tin, Loyola University Chicago, USA • Jerry Chou, National Tsing Hua University, Taiwan • Jinoh Kim, Texas A&M University-Commerce, USA Keynote Speaker (TBD) Publication Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library. Venue The conference will be held in conjunction with ACM HPDC 2024. Contact All questions about submissions should be emailed to SNTA.help@gmail.com. -- ************************************************************************************** Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining & Machine Learning Head of HPC Lab https://hpc-lab.unisalento.it Director of Master in Applied Data Science Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax  +39 0832 297371 Web   https://www.massimocafaro.it Web   https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro@unisalento.it E-mail cafaro@ieee.org E-mail cafaro@acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro@cmcc.it The sum of the intelligence on the planet is a constant; the population is growing. ************************************************************************************** --