AIPerf 2024

2nd Workshop on Artificial Intelligence for
Performance Modeling, Prediction, and Control
(combined with the 1st Workshop on Performance
Optimization in the LLM World@ICPE 2024)

May 7
London, UK

Overview

AIPerf fosters the usage of data-driven (such as probabilistic methods, machine learning, and deep learning) and optimization-based approaches to control and predict the performance of computer systems. Indeed, despite their success in different domains, AI and optimization-based techniques are still rarely adopted to manage the performance of ICT systems. This is primarily due to the lack of specialized solutions that cause an undue burden (in terms of data, time, and expertise) required to make such systems work. AIPerf aims to bring together practitioners from performance engineering, control theory, optimization, and machine learning communities to promote the dissemination of research works on novel techniques for quantitative analysis and optimization of ICT systems.
For this edition, recognizing the strong correlation between the topics, AIPerf is combined with the 1st Workshop on Performance Optimization in the LLM World. We believe that this fusion could offer mutual benefits to the audiences of both workshops, stimulating paper dissemination and fostering fruitful collaborations.

Call for Papers

We solicit two tracks of submission:

• Regular research papers (max 8 pages). Novel contributions that are fully validated and well positioned in the state-of-the-art.
• Empirical, experience, reproduction or case study papers (max 6 pages). Work-in-progress; Vision papers; New ideas that still need to be validated; Industrial case studies; Experiences (positive or negative) of using AI for performance engineering.

Scope

AIPerf 2024 welcomes submissions reporting methodological and practical research that advances and promotes the usage of Artificial Intelligence for the performance evaluation of ITC systems. The list of topics includes (but is not limited to):

• data-driven model identification for performance evaluation of ICT systems
• first principle and white-box performance modeling
• data sets and benchmarks for training and validating AI performance models
• explainability and robustness assessment of AI systems in performance engineering
• numerical, heuristics and meta-heuristic techniques for performance optimization and improvement
• AI models for performance anomaly detection, classification, root cause analysis and remediation
• AI models for performance tasks automation, including auto-scaling and self-optimization
• human-computer interfaces for AI-based performance tasks

Instructions for Authors from ACM

By submitting your article to an ACM Publication, you are hereby acknowledging that you and your co-authors are subject to all ACM Publications Policies, including ACM's new Publications Policy on Research Involving Human Participants and Subjects. Alleged violations of this policy or any ACM Publications Policy will be investigated by ACM and may result in a full retraction of your paper, in addition to other potential penalties, as per ACM Publications Policy.

Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper. ACM has been involved in ORCID from the start and we have recently made a commitment to collect ORCID IDs from all of our published authors. The collection process has started and will roll out as a requirement throughout 2022. We are committed to improve author discoverability, ensure proper attribution and contribute to ongoing community efforts around name normalization; your ORCID ID will help in these efforts.

Important Dates

Deadlines are at Midnight (AoE)

• Paper Submission: January 26, 2024 February 9, 2024
• Paper Notification of Acceptance: February 26, 2024
• Workshop: May 7, 2024

Organizers

• Emilio Incerto (IMT School For Advanced Studies), Italy
• Marin Litoiu (Lassonde School of Engineering, York University), Canada
• Daniele Masti (IMT School For Advanced Studies), Italy

To contact the chairs, you can email: AIPerf2024@easychair.org

Program Committee

• Daniele Di Pompeo (University of L'Aquila), Italy
• Joydeep Mukherjee (California Polytechnic State University), USA
• Naser Ezzati-Jivan (Brock University), Canada
• Gabriele Russo Russo (University of Rome Tor Vergata), Italy
• Andrea Vandin (Sant'Anna School of Advanced Studies), Italy
• Ioannis Stefanakos (University of York), UK
• Simone Tonini (Sant'Anna School of Advanced Studies), Italy