[agents] AIOPS2020 - Call for papers

[Alexander Acker]

Dear researcher,

we are very excited to announce the organization of the International 
Workshop for AIOPS (AIOPS2020). Following the ever greatest interest in 
that area, we are strongly devoted to the appearance of high-quality 
research papers aiming to depict the landscape for our community. Our 
goal is to bring the community together, identify the most relevant 
problems and open a possibility for further collaborations. To further 
confirm our strong interest, we choose to collocate the workshop with 
the A-ranked ICSOC (International Conference on Service-Oriented 
Computing) 2020, Dubai, UAE, 14-17.12.2020.

Therefore, we announce our call for research papers, looking for novel 
and innovative methods in the area of artificial intelligence in the 
field system operation.

More information can be found at https://aiopsworkshop.github.io/

Submissions are now open at 
https://easychair.org/conferences/?conf=aiops2020

The official Call for Papers can be found at: 
https://easychair.org/cfp/AIOPS2020

Abstract registration deadline: August 8, 2020
Submission deadline: August 16, 2020

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*CFP*

Large-scale systems of all types, such as data centers, cloud computing 
environments, edge clouds, IoT and embedded environments, are becoming 
more and more complex. Managing such systems only with human resources 
puts an enormous burden on the operators and scales poorly from an 
economic perspective. To mitigate this issue IT operators increasingly 
rely on tools from artificial intelligence for assistance.

Artificial Intelligence for IT Operations (AIOps) is an emerging field 
arising in the intersection between the research areas of machine 
learning, big data and streaming analytics, and the management of IT 
operations. The main aim is to analyze system information of different 
kinds (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 sectors.

As a result, AIOps progress towards a future standard for IT operation 
management. However, the combination of previously separate research 
fields brings many challenges. Novel modeling techniques are needed that 
help to understand the dynamics of different systems, laying out the 
basis for assessing time horizons and uncertainty for imminent SLA 
violations, the early detection of emerging problems, autonomous 
remediation, decision making, and support, and various optimization 
objectives. Furthermore, a good understanding and interpretability of 
these aiding models are especially important for building trust between 
the employed tools and the domain experts. This will result in faster 
adoption of AIOps and further increase the interest in this research field.

The main aim 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. We want to strengthen the community and unite it 
towards the goal of joining the efforts for solving the main challenges 
the field is currently facing. A consensus and adoption of the 
principles of openness and reproducibility will boost the research in 
this emerging area significantly.


List of topics:

Early anomaly, fault and failure (AFF) detection and analysis
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
Decision support
Planning under uncertainty
Predictive and prescriptive maintenance
Maintenance scheduling and on-demand maintenance planning
Fault-tolerant system control
Reliability and quality assurance
Autonomic process optimization
Energy-efficient cloud operation
Autonomous service provisioning
Explainable AI for Systems
Visual analytics and interactive machine learning
Active and life-long learning
Information and communication models for AIOps systems
Platforms: Time-series DBs, Streaming, Data Lakes
AI platforms for AIOps
Design of experiment (DoE) for different use-cases, testbeds, evaluation 
scenarios


Each paper will be reviewed by at least three members of the 
international program committee for ensuring high quality. Paper 
acceptance will be based on originality, significance, technical 
soundness, and clarity of presentation. All accepted papers will be 
included in the workshop proceedings published as part of the Lecture 
Notes in Computer Science (LNCS) series of Springer.


Organizers,

Odej Kao, Technical University Berlin

Jorge Cardoso, Huawei Research

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