Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Discovery of Cloud Applications from Logs

Ashot Harutyunyan
* ORCID logo ,
Arnak Poghosyan
* ORCID logo ,
Tigran Bunarjyan
ORCID logo ,
Marine Harutyunyan
,
Andranik Haroyan
,
Lilit Harutyunyan
,
Nelson Baloian
ORCID logo
Version 1 : Received: 9 April 2024 / Approved: 10 April 2024 / Online: 10 April 2024 (10:02:11 CEST)

How to cite: Harutyunyan, A.; Poghosyan, A.; Bunarjyan, T.; Harutyunyan, M.; Haroyan, A.; Harutyunyan, L.; Baloian, N. Discovery of Cloud Applications from Logs. Preprints 2024, 2024040701. https://doi.org/10.20944/preprints202404.0701.v1 Harutyunyan, A.; Poghosyan, A.; Bunarjyan, T.; Harutyunyan, M.; Haroyan, A.; Harutyunyan, L.; Baloian, N. Discovery of Cloud Applications from Logs. Preprints 2024, 2024040701. https://doi.org/10.20944/preprints202404.0701.v1

Abstract

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center ops solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data − network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and native constructs of VMware Aria Operations for Logs in terms of Event Types and their distributions to group those entities, potentially automatically enrich with indicative tags, and recommend for further management tasks and policies. Our methods discriminate not only diverse kinds of applications, but also their specific deployments thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in the cloud management scenarios.

Keywords

automated cloud management; application discovert; log analytics; recommender systems

Subject

Engineering, Other

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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