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AIPerf intends to foster the usage of AI (such as probabilistic methods, machine learning, and deep learning) to control, model, and predict the performance of computer systems. The timeliness and relevance of this topic reflect current and future trends toward exploiting AI-based approaches to deal with complex, large, and interconnected systems. However, AI-based approaches are rarely adopted to control the performance of ICT systems. Moreover, they are often employed in their generic form (i.e., black-box modeling) and are not specialized for the intended use. This causes the creation of models that take a lot of time and data to be created and are not easily intelligible to a domain expert. Researchers and practitioners have recently started to turn their efforts to this topic, but methods and tools (as well as data sets) that enable the adoption of AI-based solutions in performance engineering are still missing. AIPerf aims to bring together AI practitioners and performance engineers and promote the dissemination of research works that use or study AI techniques for quantitative analysis of modern ICT systems.
Artificial Intelligence and Machine Learning are widely adopted techniques to investigate several mainstream domains (e.g., computer vision, natural language processing, and even reliability analysis). However, their usage for performance modeling and evaluation is still limited, and their benefit to the performance engineering field remains unclear. AIPerf 2023 proposes a meeting venue to promote the dissemination of research works that use or study AI techniques for quantitative analysis of modern ICT systems and to engage academics and practitioners of this field. The workshop focuses on presenting experiences and results of applying AI/ML-based techniques to performance-related problems, as well as sharing performance datasets and benchmarks with the community to facilitate the development of new and more accurate learning procedures.
We solicit two tracks of submission:
AIPerf 2023 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):
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Deadlines are at Midnight (AoE)