Leveraging data-driven infrastructure management to facilitate AIOps for big data applications and operations

Mccreadie, R. et al. (2021) Leveraging data-driven infrastructure management to facilitate AIOps for big data applications and operations. In: Curry, E., Auer, S., Berre, A. J., Metzger, A., Perez, M. S. and Zillner, S. (eds.) Technologies and Applications for Big Data Value. Springer: Cham, pp. 135-158. ISBN 9783030783068 (doi: 10.1007/978-3-030-78307-5_7)

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As institutions increasingly shift to distributed and containerized application deployments on remote heterogeneous cloud/cluster infrastructures, the cost and difficulty of efficiently managing and maintaining data-intensive applications have risen. A new emerging solution to this issue is Data-Driven Infrastructure Management (DDIM), where the decisions regarding the management of resources are taken based on data aspects and operations (both on the infrastructure and on the application levels). This chapter will introduce readers to the core concepts underpinning DDIM, based on experience gained from development of the Kubernetes-based BigDataStack DDIM platform (https://bigdatastack.eu/). This chapter involves multiple important BDV topics, including development, deployment, and operations for cluster/cloud-based big data applications, as well as data-driven analytics and artificial intelligence for smart automated infrastructure self-management. Readers will gain important insights into how next-generation DDIM platforms function, as well as how they can be used in practical deployments to improve quality of service for Big Data Applications. This chapter relates to the technical priority Data Processing Architectures of the European Big Data Value Strategic Research & Innovation Agenda [33], as well as the Data Processing Architectures horizontal and Engineering and DevOps for building Big Data Value vertical concerns. The chapter relates to the Reasoning and Decision Making cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation & Deployment Agenda [34].

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Ounis, Professor Iadh and Soldatos, Professor Ioannis and Mccreadie, Dr Richard
Authors: Mccreadie, R., Soldatos, J., Fuerst, J., Argerich, M. F., Kousiouris, G., Totow, J.-D., Nieto, A. C., Navidad, B. Q., Kyriazis, D., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Published Online:01 July 2021
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Technologies and Applications for Big Data Value: 135-158
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300332BigDataStackIadh OunisEuropean Commission (EC)779747Computing Science