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<p>Dear researcher,<br>
<br>
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.<br>
<br>
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.<br>
<br>
More information can be found at <a class="moz-txt-link-freetext" href="https://aiopsworkshop.github.io/">https://aiopsworkshop.github.io/</a><br>
<br>
Submissions are now open at
<a class="moz-txt-link-freetext" href="https://easychair.org/conferences/?conf=aiops2020">https://easychair.org/conferences/?conf=aiops2020</a><br>
<br>
The official Call for Papers can be found at:
<a class="moz-txt-link-freetext" href="https://easychair.org/cfp/AIOPS2020">https://easychair.org/cfp/AIOPS2020</a><br>
<br>
Abstract registration deadline: August 8, 2020 <br>
Submission deadline: August 16, 2020<br>
</p>
<p>--------------------------------------------------------------------<br>
<br>
<b>CFP</b><br>
<br>
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.<br>
<br>
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.<br>
<br>
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.<br>
<br>
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.<br>
<br>
<br>
List of topics:<br>
<br>
Early anomaly, fault and failure (AFF) detection and analysis<br>
Self-healing, self-correction and auto-remediation<br>
Self-adaptive time-series based models for prognostics and
forecasting<br>
AFF identification, localization, and isolation<br>
Root cause analysis<br>
Adaptive fault tolerance policies<br>
Forecasting of hardware and process quality<br>
Decision support<br>
Planning under uncertainty<br>
Predictive and prescriptive maintenance<br>
Maintenance scheduling and on-demand maintenance planning<br>
Fault-tolerant system control<br>
Reliability and quality assurance<br>
Autonomic process optimization<br>
Energy-efficient cloud operation<br>
Autonomous service provisioning<br>
Explainable AI for Systems<br>
Visual analytics and interactive machine learning<br>
Active and life-long learning<br>
Information and communication models for AIOps systems <br>
Platforms: Time-series DBs, Streaming, Data Lakes<br>
AI platforms for AIOps<br>
Design of experiment (DoE) for different use-cases, testbeds,
evaluation scenarios <br>
<br>
<br>
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.<br>
<br>
<br>
Organizers,<br>
<br>
Odej Kao, Technical University Berlin<br>
<br>
Jorge Cardoso, Huawei Research<br>
</p>
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