Large-scale IT systems, such as data centres, cloud computing environments, edge clouds, IoT and embedded environments, are the key enablers of digital transformation. Managing such systems puts an enormous burden on the operators in dealing with the abundance of data, oftentimes leading to severe economic implications. To mitigate this issue IT operators increasingly rely on tools from artificial intelligence for assistance in the operation of IT systems. 

Artificial Intelligence for IT Operations (AIOps) is an emerging field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. The main goal is the analysis of system information of heterogeneous type (metrics, logs, customer input, etc) to support administrators by optimizing various objectives like prevention of SLA violation, early anomaly detection and auto-remediation, energy-efficient system operation, providing optimal QoE for customers, predictive maintenance and many more. In this field, a constantly growing interest can be observed, and thus, practical tools are developed from both the academy and industry sector. We envision that, with the advance of AIOps technologies, the IT industry will achieve significant progress and sustained and exponential growth. 

The main focus of this workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field. A major part of the workshop is to strengthen the community and unite it towards the efforts for solving the main challenges. A consensus and adoption of the principles of openness and reproducibility will boost the research in this emerging area significantly.

Topics of interest are the following:

• Early anomaly, fault and failure (AFF) detection and analysis
• Software dependability
• Self-healing, self-correction and auto-remediation
• Self-adaptive time-series based models for prognostics and forecasting
• AFF identification, localization, and isolation
• Root cause analysis
• Adaptive fault tolerance policies
• Forecasting of hardware and process quality
• Performance management
• Planning under uncertainty
• Predictive and prescriptive maintenance
• Maintenance scheduling and on-demand maintenance planning
• Alarm correlation
• Log analysis
• Fault-tolerant system control
• Resiliency, reliability, and quality assurance
• Autonomic process optimization
• Energy-efficient cloud operation
• Distributed resource management
• Autonomous service provisioning
• Visual analytics and interactive machine learning
• Active and life-long learning
• Design of experiment (DoE) and benchmarking
• Fault injection and chaos engineering
• Use-cases, testbeds, evaluation scenarios

Authors are invited to submit full papers with a maximum length of 12 pages, including references and appendices using Springer LNCS format. All accepted papers will be included in the workshop proceedings published as part of the Lecture Notes in Computer Science (LNCS) series of Springer. The guidelines can be found at: Springer conference proceedings guidelines. Paper acceptance will be based on originality, significance, technical soundness, and clarity of presentation. Each paper will be reviewed by at least three members of the international program committee for ensuring high quality. For any further information please visit our webpage AIOPS2021 (aiops2021.github.io/) or contact us at aiops2021@googlegroups.com.

Important Dates:

Paper submission deadline: September 30, 2021

Acceptance notification: October 30, 2021

Camera-ready: November 12, 2021

Early Registration Deadline: November 05, 2021

Workshop date: November 22, 2021

All deadlines are in Samoa Standard Time (SST = GMT – 11). Check the time in the SST Zone here: https://time.is/SST