JUCS - Journal of Universal Computer Science 29(11): 1298-1318, doi: 10.3897/jucs.112570
Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications
Ashot Harutyunyan‡§,
Arnak Poghosyan|,
Lilit Harutyunyan¶,
Nelli Aghajanyan#,
Tigran Bunarjyan|¤,
A.J. Han Vinck«‡ AI Lab at Yerevan State University, Yerevan, Armenia§ Institute for Informatics and Automation Problems of NAS RA, Yerevan, Armenia| Institute of Mathematics of NAS RA, Yerevan, Armenia¶ VMware, Palo Alto, United States of America# Deutsche Börse AG, Frankfurt am Main, Germany¤ Technische Universität München, Munich, Germany« University of Duisburg-Essen, Duisburg, Germany
Corresponding author:
Ashot Harutyunyan
(
aharutyunyan@vmware.com
)
© Ashot Harutyunyan, Arnak Poghosyan, Lilit Harutyunyan, Nelli Aghajanyan, Tigran Bunarjyan, A.J. Han Vinck. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Harutyunyan A, Poghosyan A, Harutyunyan L, Aghajanyan N, Bunarjyan T, Vinck A.JH (2023) Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications. JUCS - Journal of Universal Computer Science 29(11): 1298-1318. https://doi.org/10.3897/jucs.112570 |  |
AbstractAutomated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues.
KeywordsCloud infrastructures, Key Performance Indicators (KPI), automated root cause analysis (RCA), explainable ML/AI, feature importance, rule induction