Done yet? A critical introspective of the cloud management toolbox
Beitrag zu einer Konferenz
Authors
Leznik, Mark
Volpert, Simon
Griesinger, Frank
Seybold, Daniel
Domaschka, Jörg
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutions
Institut für Organisation und Management von InformationssystemenPublished in
2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) ; 2018 (2018). - ISBN 978-1-5386-1470-9, ISBN 978-1-5386-1469-3
Link to original publication
https://dx.doi.org/10.1109/ICE.2018.8436348Peer review
ja
Document version
acceptedVersion
Conference
ICE/IEEE ITMC International Conference on Engineering, Technology and Innovation, 2018-06-17 - 2018-06-20, Stuttgart
Abstract
With the rapid rise of the cloud computing paradigm, the manual maintenance and provisioning of the
technological layers behind it, both in their hardware and virtualized form, became cumbersome and error-
prone. This has opened up the need for automated capacity planning strategies in heterogeneous cloud
computing environments. However, even with mechanisms to fully accommodate customers and ful ll service-
level agreements, providers often tend to over-provision their hardware and virtual resources. A proliferation
of unused capacity leads to higher energy costs, and correspondingly, the price for cloud technology services.
Capacity planning algorithms rely on data collected from the utilized resources. Yet, the amount of data
aggregated through the monitoring of hardware and virtual instances does not allow for a manual supervision,
much less data analysis or a correlation and anomaly detection. Current data science advancements enable
the assistance of e cient automation, scheduling and provisioning of cloud computing resources based on
supervised and unsupervised machine learning techniques. In this work, we present the current state of the
art in monitoring, storage, analysis and adaptation approaches for the data produced by cloud computing
environments, to enable proactive, dynamic resource provisioning.
EU Project
MELODIC / Multi-cloud Execution-ware for Large-scale Optimized Data-Intensive Computing / EC / H2020 / 731664
RECAP / Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications / EC / H2020 / 732667
RECAP / Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications / EC / H2020 / 732667
Earlier version(s)
http://dx.doi.org/10.18725/OPARU-9631Subject Headings
Cloud Computing [GND]Data Science [GND]
Information technology; Management [LCSH]
Service-oriented architecture (Computer science) [LCSH]
Computing platforms [LCSH]
Machine learning [LCSH]
Keywords
IaaS; PaaS; AutonomicsDewey Decimal Group
DDC 004 / Data processing & computer scienceMetadata
Show full item recordCitation example
Leznik, Mark et al. (2018): Done yet? A critical introspective of the cloud management toolbox. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-10602