Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection in a Large-scale Cloud Platform   Cloud computing is ubiquitous: more and more companies are moving the
workloads into the Cloud. However, this rise in popularity challenges Cloud
service providers, as they need to monitor the quality of their ever-growing
offerings effectively. To address the challenge, we designed and implemented an
automated monitoring system for the IBM Cloud Platform. This monitoring system
utilizes deep learning neural networks to detect anomalies in near-real-time in
multiple Platform components simultaneously.
  After running the system for a year, we observed that the proposed solution
frees the DevOps team's time and human resources from manually monitoring
thousands of Cloud components. Moreover, it increases customer satisfaction by
reducing the risk of Cloud outages.
  In this paper, we share our solutions' architecture, implementation notes,
and best practices that emerged while evolving the monitoring system. They can
be leveraged by other researchers and practitioners to build anomaly detectors
for complex systems.

30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph