Discovery of Cloud Applications from Logs

Ashot Harutyunyan (), Arnak Poghosyan (), Tigran Bunarjyan, Andranik Haroyan, Marine Harutyunyan, Lilit Harutyunyan and Nelson Baloian
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Ashot Harutyunyan: Machine Learning Laboratory, Yerevan State University, Yerevan 0025, Armenia
Arnak Poghosyan: Institute of Mathematics NAS RA, Yerevan 0019, Armenia
Tigran Bunarjyan: Department of Informatics, Technical University of Munich, 80333 Munich, Germany
Andranik Haroyan: VMware by Broadcom, Yerevan 0014, Armenia
Marine Harutyunyan: VMware by Broadcom, Yerevan 0014, Armenia
Lilit Harutyunyan: VMware by Broadcom, Yerevan 0014, Armenia
Nelson Baloian: Department of Computer Science, University of Chile, Santiago 837-0456, Chile

Future Internet, 2024, vol. 16, issue 6, 1-14

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 operation 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 demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate 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 cloud management scenarios.

Keywords: automated cloud management; application discovery; dimensionality reduction; event types; hierarchical and density-based clustering; log analytics; recommender system (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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