All deadlines are at 11:59pm Pacific Time.
Digital transformation is happening in all industries. Running businesses on top of cloud services (e.g., SaaS, PaaS, IaaS) is becoming the core of digital transformation. However, the large-scale and high complexity of cloud services brings great challenges to the industry. Artificial intelligence and machine learning will play an important role in efficiently and effectively building and operating cloud services. We envision that, with the advance of AI/ML and other related technologies, the cloud industry will achieve significant progress in the following aspects while keeping a sustained and exponential growth:
The workshop targets creating an interdisciplinary forum for researchers and practitioners from the fields mentioned above. The workshop encourages submissions on innovative technologies and applications that leverage AI/ML for efficient and manageable cloud services. Topics of interest include AI/ML related techniques, methodologies, and experiences for cloud intelligence and DevOps solutions.
For each accepted paper, at least one author must attend the workshop and present the paper.
The workshop invites submission of manuscripts with original research results that have not been previously published and that are not currently under review by another conference and journal. Submissions will be assessed based on their novelty, technical quality, potential impact, interest, clarity, relevance, and reproducibility. Following AAAI conference tradition, submission reviewing is double-blinded. Submitted papers will be peer-reviewed and selected for oral or poster presentation. Accepted papers will be listed on the workshop’s website.
Submissions must be in PDF format, no more than six pages for long papers and two pages for project showcases, including all content and references, and formatted in AAAI two-column, camera-ready style (see the 2020 author kit for details).
The project showcase track focuses on innovative cloud intelligence projects. Submissions should contain a brief description of the project including its goal, problem statement, solution, and deployment status if applicable. Preference will be given to projects that contribute to open source and open data. A project URL is recommended.